Optimizing CNN Models for COVID-19 Detection: A Comparative Analysis of Optimizers and Loss Functions

Course project for CSC413: Neural Networks and Deep Learning

Our report can be found in report.pdf

Train-validation-test split:

On around 28000 training data, performed train-validation split of 80%-20%

Train the model on train set, use the model with highest validation accuracy among all epochs on test set to avoid overfitting

All evaluation metrics reported based on an unseen test dataset: 200 negative cases, 200 positive cases

Model 1: Modified ResNet 152

Best Optimizer and Loss function: RMSprop + Cross Entropy

Accuracy: 98.0%, Sensitivity: 96.0%, Specificity: 100.0%, F-1 Score: 96.6%

Run the model by executing the file resnet152/resnet152.ipynb

Model 2: Small COVID-Net

Best Optimizer and Loss function: AdamW + Cross Entropy

Accuracy: 89.5%, Sensitivity: 82.5%, Specificity: 96.5%

Run the model by executing the file small_covid_net.ipynb, preferably on Kaggle

Model 3: Shallow CNN

Best Optimizer and Loss function: SGD (Stochastic Gradient Descent) + Cross Entropy

Accuracy: 89.0%, Sensitivity: 82.0%, Specificity: 96.0%

Run the model by executing the files shallowCNN/train_ce_nll.py and shallowCNN/train_mse.py

Note: file paths in the .py or .ipynb files may need to be changed depending on the executing environment