Skip to content

Courses
Tutorials
Practice
Contests

Data Science
Data Science Projects
Data Analysis
Data Visualization
Machine Learning
ML Projects
Deep Learning
NLP
Computer Vision
Artificial Intelligence

Image Segmentation Using TensorFlow

Detecting COVID-19 From Chest X-Ray Images using CNN

Last Updated : 15 Mar, 2024

Comments

Improve

Suggest changes

Like Article

Like

Report

A Django Based Web Application built for the purpose of detecting the presence of COVID-19 from Chest X-Ray images with multiple machine learning models trained on pre-built architectures. Three different machine learning models were used to build this project namely Xception, ResNet50, and VGG16. The Deep Learning model was trained on a publicly available dataset, the SARS-COV-2-Ct-Scan Dataset. The purpose of this project is to apply Convolutional Neural Network (CNN) Architectures in solving problems of the pandemic on a preliminary stage.

Tools and Technologies Used

Some important libraries and technologies used are listed below

Programming Language: Python
Web Framework: Django
Machine Learning Framework: Tensorflow
Frontend Dev: HTML, CSS (BootStrap)
Essential Libraries: keras, sklearn, venv, seaborn, matplotlib

A detailed list of all the libraries can be found here.

Step by Step Implementation

Deep Learning Part

1) Convert Dataset into Dataframe

Convert the data into a pandas dataframe with the corresponding columns

File [Image File]
DiseaseID [Serial Number]
DiseaseType [COVID, non-COVID]

Python3

train_dir = 'path/to/dataset'
train_data = [] 
  
for defects_id, sp in enumerate(disease_types): 
    for file in os.listdir(os.path.join(train_dir, sp)): 
        train_data.append(['{}/{}'.format(sp, file), defects_id, sp]) 
          
train = pd.DataFrame(train_data, columns=['File', 'DiseaseID', 'Disease Type']) 

2) Read and Preprocess the Dataframe

Read the Images
Convert the Images into the standard size of (64 x 64)
Create numpy arrays for input/output X_Train & Y_Train
Normalize the RGB values by dividing with 255.

Python3

IMAGE_SIZE = 64
  
def read_image(filepath): 
    return cv2.imread(os.path.join(data_dir, filepath)) 
  
def resize_image(image, image_size): 
    return cv2.resize(image.copy(), image_size,  
                      interpolation=cv2.INTER_AREA) 
  
X_train = np.zeros((train.shape[0], IMAGE_SIZE, IMAGE_SIZE, 3)) 
  
for i, file in tqdm(enumerate(train['File'].values)): 
    image = read_image(file) 
    if image is not None: 
        X_train[i] = resize_image(image, (IMAGE_SIZE, IMAGE_SIZE)) 
          
X_Train = X_train / 255.
  
Y_train = train['DiseaseID'].values 
Y_train = to_categorical(Y_train, num_classes=2) 

3) Split the Dataset into Train/Validation

Split into Train, Validation datasets
Select split percentage & random state accordingly

Python3

X_train, X_val, Y_train, Y_val = train_test_split( 
  X_Train, Y_train, test_size=0.2, random_state = 42)

4) Define Model Architecture

We are going to import three different architectures listed below :
- VGG16
- ResNet50
- Xception
Structure of Model Architecture
- Conv2D of Input Shape (3,3)
- ResNet50/Xception/VGG16 Architecture
- Add a GlobalAveragePooling2D()
- Add a Dropout Layer
- Final DenseNet Layer with relu activation
- For Multiple Output add a Softmax layer
Use an ‘adam’ optimizer, hyperparameters can be tuned accordingly
The following code proposes a sample code for building a model

Python3

def build_model(): 
  
    # Use Any One of the Following Lines 
    resnet50 = ResNet50(weights='imagenet', include_top=False) 
    xception = Xception(weights='imagenet', include_top=False) 
    vgg16 = VGG16(weights='imagenet', include_top=False) 
  
    input = Input(shape=(SIZE, SIZE, N_ch)) 
    x = Conv2D(3, (3, 3), padding='same')(input) 
  
    # Use Any One of the Following Lines 
    x = resnet50(x) 
    x = xception(x) 
    x = vgg16(x) 
  
    x = GlobalAveragePooling2D()(x) 
    x = BatchNormalization()(x) 
    x = Dropout(0.5)(x) 
    x = Dense(256, activation='relu')(x) 
    x = BatchNormalization()(x) 
    x = Dropout(0.5)(x) 
  
    # multi output 
    output = Dense(2, activation='softmax', name='root')(x) 
  
    # model 
    model = Model(input, output) 
  
    optimizer = Adam(lr=0.003, beta_1=0.9, beta_2=0.999, 
                     epsilon=0.1, decay=0.0) 
      
    model.compile(loss='categorical_crossentropy', 
                  optimizer=optimizer, metrics=['accuracy']) 
      
    model.summary() 
  
    return model 

5) Train the Model

Call build_model() function
Use an annealer, a callback that monitors a quantity and if no improvement is seen for a ‘patience’ number of epochs, the learning rate is reduced.
Use an ImageDataGenerator to carry out real time image data augmentation
Train the model on x_train, y_train
Save the model weights in .hdf5 format and model graph in .json format

Python3

# Use Any one of the Lines Below 
hdf5_save = 'ResNet50_Model.hdf5'
hdf5_save = 'Xception_Model.hdf5'
hdf5_save = 'VGG16_Model.hdf5'
  
model = build_model() 
annealer = ReduceLROnPlateau( 
    monitor='val_accuracy', factor=0.70, patience=5, 
    verbose=1, min_lr=1e-4) 
  
checkpoint = ModelCheckpoint(h5f5_save, verbose=1, save_best_only=True) 
  
datagen = ImageDataGenerator(rotation_range=360, 
                             width_shift_range=0.2, 
                             height_shift_range=0.2, 
                             zoom_range=0.2, 
                             horizontal_flip=True, 
                             vertical_flip=True) 
  
datagen.fit(X_train) 
  
# Use Any one of the lines Below 
model_graph = 'ResNet50.json'
model_graph = 'Xception.json'
model_graph = 'VGG16.json'
  
model_json = model.to_json() 
with open(model_graph, "w") as json_file: 
    json_file.write(model_json) 

Building the Web App

Create a Django Project with an application initialized inside it which would be using the saved model weights to predict uploaded Chest X-Ray Images
Create a basic Static Page with a form to send the image file to the backend

HTML

<form method="post" id="imageForm" enctype="multipart/form-data"> 
   {% csrf_token %} 
   <label for="ImgFile">Upload Image</label> 
   <input type="file" name="ImgFile" class="form-control"/> 
   <input type="submit" id="submitButton" class="btn" name="submit" value="Solve"/> 
</form>

Inside the views.py folder, handle the uploaded image. Load the model files and send the response back to the front end.
The response would contain the following details
- Model Prediction
- Confidence Score
- Prediction Duration (in s)
Add styling to the frontend using CSS (Bootstrap) accordingly

Note: Loading multiple models and using model.predict() takes a lot of time and it’d be much more in the absence of GPU services in the Cloud instance. For scaling this application to a higher server load consider using TensorFlow Serving

Demo

A Demo Version of the project built and tested on localhost is demonstrated in the video below

COV-CNN Demo

Applications in Real Life & Future Work

The project built in the previous lines cannot be directly applied, however, a lot of such applications can be built on a similar tangent for serving the purpose of preliminary medical diagnosis based on inputs of patients saving a lot of screening stage costs to the medical industry. The machine learning pipeline presented in the project can be taken up a notch by making it dynamic in nature. By adding more training data dynamically to the model and train it on them to improve its accuracy. The ML Model can be converted into a REST API making the application more robust in nature and scalable. A MySQL Database could be used for storing patient data with diagnosis details and other parameters. I’ve presented a use-sketch diagram for illustrating the architecture of the application which could be built in the near future to be applied in the medical industry.

Resources

GitHub Repository : https://github.com/dwaipayan05/CovCNN-WebApp
Google Drive Link : Notebook/Weight Files/Dataset

Image Segmentation Using TensorFlow

D

dwaipayanmunshi2001

Improve

Article Tags :

AI-ML-DS
Deep Learning
ProGeek
AI-ML-DS With Python
Deep Learning Projects
ProGeek 2021

Similar Reads

100+ Machine Learning Projects with Source Code [2025]

This article provides over 100 Machine Learning projects and ideas to provide hands-on experience for both beginners and professionals. Whether you're a student enhancing your resume or a professional advancing your career these projects offer practical insights into the world of Machine Learning an

Classification Projects

Wine Quality Prediction - Machine Learning

Here we will predict the quality of wine on the basis of given features. We use the wine quality dataset available on Internet for free. This dataset has the fundamental features which are responsible for affecting the quality of the wine. By the use of several Machine learning models, we will predi

5 min read
ML | Credit Card Fraud Detection

Fraudulent credit card transactions are a significant challenge for financial institutions and consumers alike. Detecting these fraudulent activities in real-time is important to prevent financial losses and protect customers from unauthorized charges. In this article we will explore how to build a

5 min read
Disease Prediction Using Machine Learning

Disease prediction using machine learning is used in healthcare to provide accurate and early diagnosis based on patient symptoms. We can build predictive models that identify diseases efficiently. In this article, we will explore the end-to-end implementation of such a system. Step 1: Import Librar

5 min read
Recommendation System in Python

Industry leaders like Netflix, Amazon and Uber Eats have transformed how individuals access products and services. They do this by using recommendation algorithms that improve the user experience. These systems offer personalized recommendations based on users interests and preferences. In this arti

7 min read
Detecting Spam Emails Using Tensorflow in Python

Spam messages are unsolicited or unwanted emails/messages sent in bulk to users. Detecting spam emails automatically helps prevent unnecessary clutter in users' inboxes. In this article, we will build a spam email detection model that classifies emails as Spam or Ham (Not Spam) using TensorFlow, one

5 min read
SMS Spam Detection using TensorFlow in Python

In today's society, practically everyone has a mobile phone, and they all get communications (SMS/ email) on their phone regularly. But the essential point is that majority of the messages received will be spam, with only a few being ham or necessary communications. Scammers create fraudulent text m

8 min read
Python | Classify Handwritten Digits with Tensorflow

Classifying handwritten digits is the basic problem of the machine learning and can be solved in many ways here we will implement them by using TensorFlowUsing a Linear Classifier Algorithm with tf.contrib.learn linear classifier achieves the classification of handwritten digits by making a choice b

4 min read
Recognizing HandWritten Digits in Scikit Learn

Scikit learn is one of the most widely used machine learning libraries in the machine learning community the reason behind that is the ease of code and availability of approximately all functionalities which a machine learning developer will need to build a machine learning model. In this article, w

10 min read
Identifying handwritten digits using Logistic Regression in PyTorch

Logistic Regression is a very commonly used statistical method that allows us to predict a binary output from a set of independent variables. The various properties of logistic regression and its Python implementation have been covered in this article previously. Now, we shall find out how to implem

7 min read
Python | Customer Churn Analysis Prediction

Customer Churn It is when an existing customer, user, subscriber, or any kind of return client stops doing business or ends the relationship with a company. Types of Customer Churn - Contractual Churn : When a customer is under a contract for a service and decides to cancel the service e.g. Cable TV

5 min read
Online Payment Fraud Detection using Machine Learning in Python

As we are approaching modernity, the trend of paying online is increasing tremendously. It is very beneficial for the buyer to pay online as it saves time, and solves the problem of free money. Also, we do not need to carry cash with us. But we all know that Good thing are accompanied by bad things.

5 min read
Flipkart Reviews Sentiment Analysis using Python

Sentiment analysis is a NLP task used to determine the sentiment behind textual data. In context of product reviews it helps in understanding whether the feedback given by customers is positive, negative or neutral. It helps businesses gain valuable insights about customer experiences, product quali

4 min read
Loan Approval Prediction using Machine Learning

LOANS are the major requirement of the modern world. By this only, Banks get a major part of the total profit. It is beneficial for students to manage their education and living expenses, and for people to buy any kind of luxury like houses, cars, etc. But when it comes to deciding whether the appli

5 min read
Loan Eligibility Prediction using Machine Learning Models in Python

Have you ever thought about the apps that can predict whether you will get your loan approved or not? In this article, we are going to develop one such model that can predict whether a person will get his/her loan approved or not by using some of the background information of the applicant like the

5 min read
Stock Price Prediction using Machine Learning in Python

Machine learning proves immensely helpful in many industries in automating tasks that earlier required human labor one such application of ML is predicting whether a particular trade will be profitable or not. In this article, we will learn how to predict a signal that indicates whether buying a par

8 min read
Bitcoin Price Prediction using Machine Learning in Python

Machine learning proves immensely helpful in many industries in automating tasks that earlier required human labor one such application of ML is predicting whether a particular trade will be profitable or not. In this article, we will learn how to predict a signal that indicates whether buying a par

7 min read
Handwritten Digit Recognition using Neural Network

Handwritten digit recognition is a classic problem in machine learning and computer vision. It involves recognizing handwritten digits (0-9) from images or scanned documents. This task is widely used as a benchmark for evaluating machine learning models especially neural networks due to its simplici

5 min read
Parkinson Disease Prediction using Machine Learning - Python

Parkinson's disease is a progressive neurological disorder that affects movement. Stiffening, tremors and slowing down of movements may be signs of Parkinson's disease. While there is no certain diagnostic test, but we can use machine learning in predicting whether a person has Parkinson's disease b

8 min read
Spaceship Titanic Project using Machine Learning - Python

If you are a machine learning enthusiast you must have done the Titanic project in which you would have predicted whether a person will survive or not.Â Spaceship Titanic Project using Machine Learning in PythonIn this article, we will try to solve one such problem which is a slightly modified versi

9 min read
Rainfall Prediction using Machine Learning - Python

Today there are no certain methods by using which we can predict whether there will be rainfall today or not. Even the meteorological department's prediction fails sometimes. In this article, we will learn how to build a machine-learning model which can predict whether there will be rainfall today o

7 min read
Autism Prediction using Machine Learning

Autism is a neurological disorder that affects a person's ability to interact with others, make eye contact with others, learn and have other behavioral issue. However there is no certain way to tell whether a person has Autism or not because there are no such diagnostics methods available to diagno

8 min read
Predicting Stock Price Direction using Support Vector Machines

We are going to implement an End-to-End project using Support Vector Machines to live Trade For us. You Probably must have Heard of the term stock market which is known to have made the lives of thousands and to have destroyed the lives of millions. If you are not familiar with the stock market you

5 min read
Fake News Detection Model using TensorFlow in Python

Fake news is a type of misinformation that can mislead readers, influence public opinion, and even damage reputations. Detecting fake news prevents its spread and protects individuals and organizations. Media outlets often use these models to help filter and verify content, ensuring that the news sh

5 min read
CIFAR-10 Image Classification in TensorFlow

Prerequisites:Image ClassificationConvolution Neural Networks including basic pooling, convolution layers with normalization in neural networks, and dropout.Data Augmentation.Neural Networks.Numpy arrays.In this article, we are going to discuss how to classify images using TensorFlow. Image Classifi

8 min read
Black and white image colorization with OpenCV and Deep Learning

In this article, we'll create a program to convert a black & white image i.e grayscale image to a colour image. We're going to use the Caffe colourization model for this program. And you should be familiar with basic OpenCV functions and uses like reading an image or how to load a pre-trained mo

3 min read
ML | Breast Cancer Wisconsin Diagnosis using Logistic Regression

Breast Cancer Wisconsin Diagnosis dataset is commonly used in machine learning to classify breast tumors as malignant (cancerous) or benign (non-cancerous) based on features extracted from breast mass images. In this article we will apply Logistic Regression algorithm for binary classification to pr

5 min read
ML | Cancer cell classification using Scikit-learn

Machine learning is used in solving real-world problems including medical diagnostics. One such application is classifying cancer cells based on their features and determining whether they are 'malignant' or 'benign'. In this article, we will use Scikit-learn to build a classifier for cancer cell de

4 min read
ML | Kaggle Breast Cancer Wisconsin Diagnosis using KNN and Cross Validation

Dataset : It is given by Kaggle from UCI Machine Learning Repository, in one of its challenges. It is a dataset of Breast Cancer patients with Malignant and Benign tumor. K-nearest neighbour algorithm is used to predict whether is patient is having cancer (Malignant tumour) or not (Benign tumour). I

3 min read
Human Scream Detection and Analysis for Controlling Crime Rate - Project Idea

Project Title: Human Scream Detection and Analysis for Controlling Crime Rate using Machine Learning and Deep Learning Crime is the biggest social problem of our society which is spreading day by day. Thousands of crimes are committed every day, and still many are occurring right now also all over t

6 min read
Multiclass image classification using Transfer learning

Image classification is one of the supervised machine learning problems which aims to categorize the images of a dataset into their respective categories or labels. Classification of images of various dog breeds is a classic image classification problem. So, we have to classify more than one class t

9 min read
Intrusion Detection System Using Machine Learning Algorithms

Problem Statement: The task is to build a network intrusion detector, a predictive model capable of distinguishing between bad connections, called intrusions or attacks, and good normal connections. Introduction: Intrusion Detection System is a software application to detect network intrusion using

11 min read
Heart Disease Prediction using ANN

Deep Learning is a technology of which mimics a human brain in the sense that it consists of multiple neurons with multiple layers like a human brain. The network so formed consists of an input layer, an output layer, and one or more hidden layers. The network tries to learn from the data that is fe

3 min read

Regression Projects

IPL Score Prediction using Deep Learning

In the modern era of cricket analytics, where each run and decision can change the outcome, the application of Deep Learning for IPL score prediction stands at the forefront of innovation. This article explores the cutting-edge use of advanced algorithms to forecast IPL score in live matches with hi

7 min read
Dogecoin Price Prediction with Machine Learning

Dogecoin is a cryptocurrency, like Ethereum or Bitcoin â€” despite the fact that it's totally different than both of these famous coins. Dogecoin was initially made to some extent as a joke for crypto devotees and took its name from a previously well-known meme. In this article, we will be implementin

4 min read
Zillow Home Value (Zestimate) Prediction in ML

In this article, we will try to implement a house price index calculator which revolutionized the whole real estate industry in the US. This will be a regression task in which we have been provided with logarithm differences between the actual and the predicted prices of those homes by using a bench

6 min read
Calories Burnt Prediction using Machine Learning

In this article, we will learn how to develop a machine learning model using Python which can predict the number of calories a person has burnt during a workout based on some biological measures. Importing Libraries and DatasetPython libraries make it easy for us to handle the data and perform typic

5 min read
Vehicle Count Prediction From Sensor Data

Sensors at road junctions collect vehicle count data at different times which helps transport managers make informed decisions. In this article we will predict vehicle count based on this sensor data using machine learning techniques. Implementation of Vehicle Count PredictionDataset which we will b

3 min read
Analyzing Selling Price of used Cars using Python

Analyzing the selling price of used cars is essential for making informed decisions in the automotive market. Using Python, we can efficiently process and visualize data to uncover key factors influencing car prices. This analysis not only aids buyers and sellers but also enables predictive modeling

4 min read
Box Office Revenue Prediction Using Linear Regression in ML

When a movie is produced then the director would certainly like to maximize his/her movie's revenue. But can we predict what will be the revenue of a movie by using its genre or budget information? This is exactly what we'll learn in this article, we will learn how to implement a machine learning al

6 min read
House Price Prediction using Machine Learning in Python

House price prediction is a problem in the real estate industry to make informed decisions. By using machine learning algorithms we can predict the price of a house based on various features such as location, size, number of bedrooms and other relevant factors. In this article we will explore how to

6 min read
Linear Regression using Boston Housing Dataset - ML

Boston Housing Data: This dataset was taken from the StatLib library and is maintained by Carnegie Mellon University. This dataset concerns the housing prices in the housing city of Boston. The dataset provided has 506 instances with 13 features.The Description of the dataset is taken from the below

3 min read
Stock Price Prediction Project using TensorFlow

Stock price prediction is a challenging task in the field of finance with applications ranging from personal investment strategies to algorithmic trading. In this article we will explore how to build a stock price prediction model using TensorFlow and Long Short-Term Memory (LSTM) networks a type of

5 min read
Medical Insurance Price Prediction using Machine Learning - Python

You must have heard some advertisements regarding medical insurance that promises to help financially in case of any medical emergency. One who purchases this type of insurance has to pay premiums monthly and this premium amount varies vastly depending upon various factors.Â Medical Insurance Price

7 min read
Inventory Demand Forecasting using Machine Learning - Python

Vendors selling everyday items need to keep their stock updated so that customers donâ€™t leave empty-handed. Maintaining the right stock levels helps avoid shortages that disappoint customers and prevents overstocking which can increase costs. In this article weâ€™ll learn how to use Machine Learning (

6 min read
Ola Bike Ride Request Forecast using ML

From telling rickshaw-wala where to go, to tell him where to come we have grown up. Yes, we are talking about online cab and bike facility providers like OLA and Uber. If you had used this app some times then you must have paid some day less and someday more for the same journey. But have you ever t

8 min read
Waiter's Tip Prediction using Machine Learning

If you have recently visited a restaurant for a family dinner or lunch and you have tipped the waiter for his generous behavior then this project might excite you. As in this article, we will try to predict what amount of tip a person will give based on his/her visit to the restaurant using some fea

7 min read
Predict Fuel Efficiency Using Tensorflow in Python

Predicting fuel efficiency is a important task in automotive design and environmental sustainability. In this article we will build a fuel efficiency prediction model using TensorFlow one of the most popular deep learning libraries. We will use the Auto MPG dataset which contains features like engin

5 min read
Microsoft Stock Price Prediction with Machine Learning

In this article, we will implement Microsoft Stock Price Prediction with a Machine Learning technique. We will use TensorFlow, an Open-Source Python Machine Learning Framework developed by Google. TensorFlow makes it easy to implement Time Series forecasting data. Since Stock Price Prediction is one

5 min read
Share Price Forecasting Using Facebook Prophet

Time series forecast can be used in a wide variety of applications such as Budget Forecasting, Stock Market Analysis, etc. But as useful it is also challenging to forecast the correct projections, Thus can't be easily automated because of the underlying assumptions and factors. The analysts who prod

6 min read
Implementation of Movie Recommender System - Python

Recommender Systems provide personalized suggestions for items that are most relevant to each user by predicting preferences according to user's past choices. They are used in various areas like movies, music, news, search queries, etc. These recommendations are made in two ways: Collaborative filte

4 min read
How can Tensorflow be used with abalone dataset to build a sequential model?

In this article, we will learn how to build a sequential model using TensorFlow in Python to predict the age of an abalone. We may wonder what is an abalone. Answer to this question is that it is a kind of snail. Generally, the age of an Abalone is determined by the physical examination of the abalo

8 min read

Computer Vision Projects

OCR of Handwritten digits | OpenCV

OCR which stands for Optical Character Recognition is a computer vision technique used to identify the different types of handwritten digits that are used in common mathematics. To perform OCR in OpenCV we will use the KNN algorithm which detects the nearest k neighbors of a particular data point an

2 min read
Cartooning an Image using OpenCV - Python

Instead of sketching images by hand we can use OpenCV to convert a image into cartoon image. In this tutorial you'll learn how to turn any image into a cartoon. We will apply a series of steps like: Smoothing the image (like a painting)Detecting edges (like a sketch)Combining both to get a cartoon e

2 min read
Count number of Object using Python-OpenCV

In this article, we will use image processing to count the number of Objects using OpenCV in Python. Google Colab link: https://colab.research.google.com/drive/10lVjcFhdy5LVJxtSoz18WywM92FQAOSV?usp=sharing Module neededOpenCv: OpenCv is an open-source library that is useful for computer vision appli

3 min read
Count number of Faces using Python - OpenCV

Prerequisites: Face detection using dlib and openCV In this article, we will use image processing to detect and count the number of faces. We are not supposed to get all the features of the face. Instead, the objective is to obtain the bounding box through some methods i.e. coordinates of the face i

3 min read
Text Detection and Extraction using OpenCV and OCR

Optical Character Recognition (OCR) is a technology used to extract text from images which is used in applications like document digitization, license plate recognition and automated data entry. In this article, we explore how to detect and extract text from images using OpenCV for image processing

2 min read
FaceMask Detection using TensorFlow in Python

In this article, weâ€™ll discuss our two-phase COVID-19 face mask detector, detailing how our computer vision/deep learning pipeline will be implemented. Weâ€™ll use this Python script to train a face mask detector and review the results. Given the trained COVID-19 face mask detector, weâ€™ll proceed to i

9 min read
Dog Breed Classification using Transfer Learning

In this tutorial, we will demonstrate how to build a dog breed classifier using transfer learning. This method allows us to use a pre-trained deep learning model and fine-tune it to classify images of different dog breeds. Why to use Transfer Learning for Dog Breed ClassificationTransfer learning is

9 min read
Flower Recognition Using Convolutional Neural Network

Convolutional Neural Network (CNN) are a type of deep learning model specifically designed for processing structured grid data such as images. In this article we will build a CNN model to classify different types of flowers from a dataset containing images of various flowers like roses, daisies, dan

6 min read
Emojify using Face Recognition with Machine Learning

In this article, we will learn how to implement a modification app that will show an emoji of expression which resembles the expression on your face. This is a fun project based on computer vision in which we use an image classification model in reality to classify different expressions of a person.

7 min read
Cat & Dog Classification using Convolutional Neural Network in Python

Convolutional Neural Networks (CNNs) are a type of deep learning model specifically designed for processing images. Unlike traditional neural networks CNNs uses convolutional layers to automatically and efficiently extract features such as edges, textures and patterns from images. This makes them hi

5 min read
Traffic Signs Recognition using CNN and Keras in Python

We always come across incidents of accidents where drivers' Overspeed or lack of vision leads to major accidents. In winter, the risk of road accidents has a 40-50% increase because of the traffic signs' lack of visibility. So here in this article, we will be implementing Traffic Sign recognition us

6 min read
Lung Cancer Detection using Convolutional Neural Network (CNN)

Computer Vision is one of the applications of deep neural networks that helps us to automate tasks that earlier required years of expertise and one such use in predicting the presence of cancerous cells. In this article, we will learn how to build a classifier using a simple Convolution Neural Netwo

7 min read
Lung Cancer Detection Using Transfer Learning

Computer Vision is one of the applications of deep neural networks that enables us to automate tasks that earlier required years of expertise and one such use in predicting the presence of cancerous cells. In this article, we will learn how to build a classifier using the Transfer Learning technique

8 min read
Pneumonia Detection using Deep Learning

In this article, we will discuss solving a medical problem i.e. Pneumonia which is a dangerous disease that may occur in one or both lungs usually caused by viruses, fungi or bacteria. We will detect this lung disease based on the x-rays we have. Chest X-rays dataset is taken from Kaggle which conta

7 min read
Detecting Covid-19 with Chest X-ray

COVID-19 pandemic is one of the biggest challenges for the healthcare system right now. It is a respiratory disease that affects our lungs and can cause lasting damage to the lungs that led to symptoms such as difficulty in breathing and in some cases pneumonia and respiratory failure. In this artic

9 min read
Skin Cancer Detection using TensorFlow

In this article, we will learn how to implement a Skin Cancer Detection model using Tensorflow. We will use a dataset that contains images for the two categories that are malignant or benign. We will use the transfer learning technique to achieve better results in less amount of training. We will us

5 min read
Age Detection using Deep Learning in OpenCV

The task of age prediction might sound simple at first but it's quite challenging in real-world applications. While predicting age is typically seen as a regression problem this approach faces many uncertainties like camera quality, brightness, climate condition, background, etc. In this article we'

5 min read
Face and Hand Landmarks Detection using Python - Mediapipe, OpenCV

In this article, we will use mediapipe python library to detect face and hand landmarks. We will be using a Holistic model from mediapipe solutions to detect all the face and hand landmarks. We will be also seeing how we can access different landmarks of the face and hands which can be used for diff

4 min read
Detecting COVID-19 From Chest X-Ray Images using CNN

A Django Based Web Application built for the purpose of detecting the presence of COVID-19 from Chest X-Ray images with multiple machine learning models trained on pre-built architectures. Three different machine learning models were used to build this project namely Xception, ResNet50, and VGG16. T

5 min read
Image Segmentation Using TensorFlow

Image segmentation refers to the task of annotating a single class to different groups of pixels. While the input is an image, the output is a mask that draws the region of the shape in that image. Image segmentation has wide applications in domains such as medical image analysis, self-driving cars,

8 min read
License Plate Recognition with OpenCV and Tesseract OCR

License Plate Recognition is widely used for automated identification of vehicle registration plates for security purpose and law enforcement. By combining computer vision techniques with Optical Character Recognition (OCR) we can extract license plate numbers from images enabling applications in ar

5 min read
Detect and Recognize Car License Plate from a video in real time

Recognizing a Car License Plate is a very important task for a camera surveillance-based security system. We can extract the license plate from an image using some computer vision techniques and then we can use Optical Character Recognition to recognize the license number. Here I will guide you thro

11 min read
Residual Networks (ResNet) - Deep Learning

After the first CNN-based architecture (AlexNet) that win the ImageNet 2012 competition, Every subsequent winning architecture uses more layers in a deep neural network to reduce the error rate. This works for less number of layers, but when we increase the number of layers, there is a common proble

9 min read

Natural Language Processing Projects

Twitter Sentiment Analysis using Python

This article covers the sentiment analysis of any topic by parsing the tweets fetched from Twitter using Python. What is sentiment analysis? Sentiment Analysis is the process of 'computationally' determining whether a piece of writing is positive, negative or neutral. Itâ€™s also known as opinion mini

10 min read
Facebook Sentiment Analysis using python

This article is a Facebook sentiment analysis using Vader, nowadays many government institutions and companies need to know their customers' feedback and comment on social media such as Facebook. What is sentiment analysis? Sentiment analysis is one of the best modern branches of machine learning, w

6 min read
Next Sentence Prediction using BERT

Next Sentence Prediction is a pre-training task used in BERT to help the model understand the relationship between different sentences. It is widely used for tasks like question answering, summarization and dialogue systems. The goal is to determine whether a given second sentence logically follows

5 min read
Hate Speech Detection using Deep Learning

There must be times when you have come across some social media post whose main aim is to spread hate and controversies or use abusive language on social media platforms. As the post consists of textual information to filter out such Hate Speeches NLP comes in handy. This is one of the main applicat

7 min read
Image Caption Generator using Deep Learning on Flickr8K dataset

Generating a caption for a given image is a challenging problem in the deep learning domain. In this article we will use different computer vision and NLP techniques to recognize the context of an image and describe them in a natural language like English. We will build a working model of the image

12 min read
Movie recommendation based on emotion in Python

Movies that effectively portray and explore emotions resonate deeply with audiences because they tap into our own emotional experiences and vulnerabilities. A well-crafted emotional movie can evoke empathy, understanding, and self-reflection, allowing viewers to connect with the characters and their

4 min read
Speech Recognition in Python using Google Speech API

Speech recognition means converting spoken words into text. It used in various artificial intelligence applications such as home automation, speech to text, etc. In this article, youâ€™ll learn how to do basic speech recognition in Python using the Google Speech Recognition API. Step 1: Install Requir

2 min read
Voice Assistant using python

As we know Python is a suitable language for scriptwriters and developers. Letâ€™s write a script for Voice Assistant using Python. The query for the assistant can be manipulated as per the userâ€™s need. Speech recognition is the process of converting audio into text. This is commonly used in voice ass

11 min read
Human Activity Recognition - Using Deep Learning Model

Human activity recognition using smartphone sensors like accelerometer is one of the hectic topics of research. HAR is one of the time series classification problem. In this project various machine learning and deep learning models have been worked out to get the best final result. In the same seque

6 min read
Fine-tuning BERT model for Sentiment Analysis

Google created a transformer-based machine learning approach for natural language processing pre-training called Bidirectional Encoder Representations from Transformers. It has a huge number of parameters, hence training it on a small dataset would lead to overfitting. This is why we use a pre-train

7 min read
Sentiment Classification Using BERT

BERT stands for Bidirectional Representation for Transformers and was proposed by researchers at Google AI language in 2018. Although the main aim of that was to improve the understanding of the meaning of queries related to Google Search, BERT becomes one of the most important and complete architec

13 min read
Sentiment Analysis with an Recurrent Neural Networks (RNN)

Recurrent Neural Networks (RNNs) excel in sequence tasks such as sentiment analysis due to their ability to capture context from sequential data. In this article we will be apply RNNs to analyze the sentiment of customer reviews from Swiggy food delivery platform. The goal is to classify reviews as

3 min read
Building an Autocorrector Using NLP in Python

Autocorrect feature predicts and correct misspelled words, it helps to save time invested in the editing of articles, emails and reports. This feature is added many websites and social media platforms to ensure easy typing. In this tutorial we will build a Python-based autocorrection feature using N

4 min read
Python | NLP analysis of Restaurant reviews

Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data. It is the branch of mach

7 min read
Restaurant Review Analysis Using NLP and SQLite

Normally, a lot of businesses are remained as failures due to lack of profit, lack of proper improvement measures. Mostly, restaurant owners face a lot of difficulties to improve their productivity. This project really helps those who want to increase their productivity, which in turn increases thei

9 min read
Twitter Sentiment Analysis using Python

This article covers the sentiment analysis of any topic by parsing the tweets fetched from Twitter using Python. What is sentiment analysis? Sentiment Analysis is the process of 'computationally' determining whether a piece of writing is positive, negative or neutral. Itâ€™s also known as opinion mini

10 min read

Clustering Projects

Customer Segmentation using Unsupervised Machine Learning in Python

Customer Segmentation involves grouping customers based on shared characteristics, behaviors and preferences. By segmenting customers, businesses can tailor their strategies and target specific groups more effectively and enhance overall market value. Today we will use Unsupervised Machine Learning

5 min read
Music Recommendation System Using Machine Learning

When did we see a video on youtube let's say it was funny then the next time you open your youtube app you get recommendations of some funny videos in your feed ever thought about how? This is nothing but an application of Machine Learning using which recommender systems are built to provide persona

4 min read
K means Clustering - Introduction

K-Means Clustering is an Unsupervised Machine Learning algorithm which groups the unlabeled dataset into different clusters. The article aims to explore the fundamentals and working of k means clustering along with its implementation. Understanding K-means ClusteringK-means clustering is a technique

6 min read
Image Segmentation using K Means Clustering

Image segmentation is a technique in computer vision that divides an image into different segments. This can help identify specific objects, boundaries or patterns in the image.Â Image is basically a set of given pixels and in image segmentation pixels with similar intensity are grouped together. Im

2 min read

Recommender System Project

AI Driven Snake Game using Deep Q Learning

Content has been removed from this Article

1 min read

Corporate & Communications Address:

A-143, 7th Floor, Sovereign Corporate Tower, Sector- 136, Noida, Uttar Pradesh (201305)

Registered Address:

K 061, Tower K, Gulshan Vivante Apartment, Sector 137, Noida, Gautam Buddh Nagar, Uttar Pradesh, 201305

Advertise with us

Company
About Us
Legal
Privacy Policy
In Media
Contact Us
Advertise with us
GFG Corporate Solution
Placement Training Program

Languages
Python
Java
C++
PHP
GoLang
SQL
R Language
Android Tutorial
Tutorials Archive

DSA
Data Structures
Algorithms
DSA for Beginners
Basic DSA Problems
DSA Roadmap
Top 100 DSA Interview Problems
DSA Roadmap by Sandeep Jain
All Cheat Sheets

Data Science & ML
Data Science With Python
Data Science For Beginner
Machine Learning
ML Maths
Data Visualisation
Pandas
NumPy
NLP
Deep Learning

Web Technologies
HTML
CSS
JavaScript
TypeScript
ReactJS
NextJS
Bootstrap
Web Design

Python Tutorial
Python Programming Examples
Python Projects
Python Tkinter
Python Web Scraping
OpenCV Tutorial
Python Interview Question
Django

Computer Science
Operating Systems
Computer Network
Database Management System
Software Engineering
Digital Logic Design
Engineering Maths
Software Development
Software Testing

DevOps
Git
Linux
AWS
Docker
Kubernetes
Azure
GCP
DevOps Roadmap

System Design
High Level Design
Low Level Design
UML Diagrams
Interview Guide
Design Patterns
OOAD
System Design Bootcamp
Interview Questions

Inteview Preparation
Competitive Programming
Top DS or Algo for CP
Company-Wise Recruitment Process
Company-Wise Preparation
Aptitude Preparation
Puzzles

School Subjects
Mathematics
Physics
Chemistry
Biology
Social Science
English Grammar
Commerce
World GK

GeeksforGeeks Videos
DSA
Python
Java
C++
Web Development
Data Science
CS Subjects

@GeeksforGeeks, Sanchhaya Education Private Limited, All rights reserved

We use cookies to ensure you have the best browsing experience on our website. By using our site, you acknowledge that you have read and understood our Cookie Policy & Privacy Policy

Improvement

Suggest Changes

Help us improve. Share your suggestions to enhance the article. Contribute your expertise and make a difference in the GeeksforGeeks portal.

geeksforgeeks-suggest-icon

Create Improvement

Enhance the article with your expertise. Contribute to the GeeksforGeeks community and help create better learning resources for all.

geeksforgeeks-improvement-icon

Suggest Changes

min 4 words, max Words Limit:1000

Thank You!

Your suggestions are valuable to us.

What kind of Experience do you want to share?

Interview Experiences

Admission Experiences

Career Journeys

Work Experiences

Campus Experiences

Competitive Exam Experiences