Chest X Ray Pneumonia Detection Using Deep Neural Network



Question No.1.

Vision Dataset: The dataset is organized into 3 folders (train, test, val) and contains subfolders for each image category (Pneumonia/Normal). There are 5,863 X-Ray images (JPEG) and 2 categories (Pneumonia/Normal). Please find your dataset from the link - https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia

(Links to an external site.)


Steps For Implementation

1. Import Libraries/Dataset

  • Import the required libraries and the dataset (use Google Drive if required).

  • Check the GPU available (recommended- use free GPU provided by Google Colab).


2. Data Visualization and augmentation

  • Plot at least two samples from each class of the dataset (use matplotlib/seaborn/any other library).

  • Apply rotation and height shift augmentation (rotation_range, height_shift_range)to the dataset separately. Print the augmented image and the original image for each class and each augmentation.

  • Bring the train and test data in the required format.

  • Print the shapes of train and test data.

 

3. Model Building

  • Sequential Model layers- Use AT LEAST 3 hidden layers with appropriate input for each. Choose the best number for hidden units and give reasons.

  • Add L2 regularization to all the layers.

  • Add one layer of dropout at the appropriate position and give reasons.

  • Choose the appropriate activation function for all the layers.

  • Print the model summary.


4. Model Compilation

  • Compile the model with the appropriate loss function.

  • Use an appropriate optimizer. Give reasons for the choice of learning rate and its value.

  • Use accuracy as a metric.

 

5. Model Training

  • Train the model for an appropriate number of epochs. Print the train and validation accuracy and loss for each epoch. Use the appropriate batch size.

  • Plot the loss and accuracy history graphs for both train and validation set. Print the total time taken for training.


6. Model Evaluation

  • Print the final train and validation loss and accuracy. Print confusion matrix and classification report for the validation dataset. Analyse and report the best and worst performing class.

  • Print the two most incorrectly classified images for each class in the test dataset.

Hyperparameter Tuning- 

Build two more additional models by changing the following hyperparameters ONE at a time. Write the code for Model Building, Model Compilation, Model Training and Model Evaluation as given in the instructions above for each additional model. 

  1. Optimiser: Use a different optimizer with the appropriate LR value.

  2. Network Depth: Change the number of hidden layers and hidden units for each layer.

Write a comparison between each model and give reasons for the difference in results.


Question No.2. Dataset:  (Data set)

  • Load the attached csv file in python. Each row consists of feature 1, feature 2, feature 3 & class label.

  • Train two single/double hidden layer deep networks by varying the number of hidden nodes (4, 8, 12, 16) in each layer with 70% training and 30% validation data. Use appropriate learning rate, activation, and loss functions and also mention the reason for choosing the same. Report, compare, and explain the observed accuracy and minimum loss achieved.

  • Visually observe the dataset and design an appropriate feature transformation (derived feature) such that after feature transformation, the dataset can be classified using a minimal network architecture (minimum number of parameters). Design, train this minimal network, and report training and validation errors, and trained parameters of the network. Use 70% training and 30% validation data, appropriate learning rate, activation and loss functions. Explain the final results.


Evaluation Process -

  1. Task Response and Task Completion- All the models should be logically sound and have decent accuracy (models with random guessing, frozen and incorrect accuracy, exploding gradients etc. will lead to deduction of marks. Please do a sanity check of your model and results before submission).

  2. There are a lot of subparts, so answer each completely and correctly, as no partial marks will be awarded for partially correct subparts.

  3. Implementation- The model layers, parameters, hyperparameters, evaluation metrics etc. should be properly implemented.

  4. Only fully connected or dense layers are allowed. CNNs/RNNs are strictly not allowed.

  5. Notebooks without output will not be considered for evaluation.






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