Detection of Lung Infection

Objective:

To build a model using a convolutional neural network that can classify lung infection in a person using medical imagery

Dataset Description:

Link to Dataset : Dataset_Detection_of_Lung_Infection
The dataset contains three different classes, including healthy, type 1 disease, and type 2 disease.
Train folder: This folder has images for training the model, which is divided into subfolders having the same name as the class.
Test folder: This folder has images for testing the model, which is divided into subfolders having the same name as the class.

Steps :

• Import the necessary libraries
• Plot the sample images for all the classes
• Plot the distribution of images across the classes
• Build a data augmentation for train data to create new data with translation, rescale and flip, and rotation transformations. Rescale the image at 48x48
• Build a data augmentation for test data to create new data and rescale the image at 48x48
• Read images directly from the train folder and test folder using the appropriate function

Build 3 CNN model with:

CNN Architecture:

• Add convolutional layers with different filters, max pool layers, dropout layers, and batch normalization layers
• Use Relu as an activation function
• Take the loss function as categorical cross-entropy
• Take rmsprop as an optimizer
• Use early stopping with the patience of two epochs and monitor the validation loss or accuracy
• Try with ten numbers epoch
• Train the model using a generator and test the accuracy of the test data at every epoch
• Plot the training and validation accuracy, and the loss
• Observe the precision, recall the F1-score for all classes for both grayscale and color models, and determine if the model’s classes are good

Transfer learning using mobile net:

• Prepare data for the pre-trained mobile net model, with color mode as RGB
• Create an instance of a mobile net pre-trained model
• Add dense layer, dropout layer, batch normalization layer on the pre-trained model
• Create a final output layer with a SoftMax activation function
• Change the batch size activation function and optimize as rmsprop and observe if the accuracy increases
• Take the loss function as categorical cross-entropy
• Use early stopping with the patience of two epoch and call back function for preventing overfitting
• Try with ten numbers epoch
• Train the model using a generator and test the accuracy of the test data at every epoch
• Plot the training and validation accuracy, and the loss
• Observe the precision, recall the F1-score for all classes for both grayscale and color models, and determine if the model’s classes are good

Transfer Learning using Densenet121:

• Prepare the dataset for the transfer learning algorithm using Densenet121 with the image size as 224x224x3
• Freeze the top layers of the pre-trained model
• Add a dense layer at the end of the pre-trained model followed by a dropout layer and try various combinations to get an accuracy
• Add the final output layer with a SoftMax activation function
• Take loss function as categorical cross-entropy
• Take Adam as an optimizer
• Use early stopping to prevent overfitting
• Try with 15 number of epoch and batch size with seven, also try various values to see the impact on results
• Train the model using the generator and test the accuracy of the test data at every epoch
• Plot the training and validation accuracy, and the loss
• Observe the precision, recall the F1-score for all classes for both grayscale and color models, and determine if the model’s classes are good

Setup and Installation:

pip install --upgrade pip
pip install -r requirements.txt
pip list