Cat and Dog Classification and Lung Cancer Detection Using CNN

Cat and Dog Classification and Lung Cancer Detection Using CNN Quiz

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Question 1

What is the primary advantage of using Convolutional Neural Networks (CNNs) for image classification tasks?

  • They are faster than traditional machine learning models

  • They automatically extract features from raw images

  • They require less training data

  • They are suitable only for small images

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Question 2

Which layer in a CNN is responsible for detecting low-level features like edges and textures?

  • Fully connected layer

  • Convolutional layer

  • Pooling layer

  • Dropout layer

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Question 3

For classifying cats and dogs, what is typically the output layer for the CNN model?

  • Dense layer with a softmax activation

  • Convolutional layer with a ReLU activation

  • Pooling layer with a sigmoid activation

  • Dense layer with a sigmoid activation

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Question 4

What is the purpose of data augmentation in the context of cat and dog classification using CNN?

  • To create additional training data by transforming existing images

  • To reduce the number of features in the images

  • To make the model run faster

  • To increase the batch size during training

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Question 5

Which of the following is a common pre-processing step before feeding images into a CNN for lung cancer detection?

  • Normalizing the image pixel values

  • Reducing the size of the image to a fixed dimension

  • Converting the image to grayscale

  • All of the above

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There are 5 questions to complete.

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