COV19 Detection on CT Scans

The project aims to analyze computed tomography (CT) images and classify them into one of three classes: COV, Normal, OtherPneumonia. The dataset is publicly available under this link (data version used in the project: link).

The project consists of Jupyter notebooks and additional py files with model architectures and some useful functions.

Initial analysis and preprocessing

The analysis of the dataset and other visualisations can be found in:

• Analysis and Visualisations - analysis of the dataset
• Data Augmentation - images after data augmentation

Preprocessing steps are presented in:

• Patient Segmentation - finding patient's contour and background removal

• Lungs Segmentation - lungs segmentation using skimage segmentation

Model configurations

Several model architectures have been used:

• Simple model created from scratch,
• Tiny, Small, LargeW, LargeT architectures from Transfusion: Understanding Transfer Learning for Medical Imaging,
• ResNet-50 from Google Big Transfer,
• EfficientNet B0, B3, B7.

Models were trained on images at different stages of processing:

The influence of class weight (classw) and data augmentation (dataaug) were examined. The lack of these methods is referred to as baseline.

Results

The highest f1 scores achieved on the validation set:

Model	Configuration	F1 Score	AUC
Simple	original-classw-dataaug	0.810	0.925
Tiny	original-baseline	0.861	0.954
Small	original-baseline	0.860	0.944
LargeW	original-classw	0.853	0.944
LargeT	orginal-baseline	0.888	0.958
ResNet-50	original-baseline	0.766	0.893
EfficientNet B3 - weights ImageNet	lungs-baseline	0.768	0.896
EfficientNet B3 - weights None	lungs-nocrop-baseline	0.761	0.866

Scores achieved on the testset by the above best models:

Model	Configuration	F1 Score	AUC
Simple	original-classw-dataaug	0.582	0.810
Tiny	original-baseline	0.580	0.811
Small	original-baseline	0.600	0.798
LargeW	original-classw	0.634	0.792
LargeT	orginal-baseline	0.577	0.796
ResNet-50	original-baseline	0.711	0.886
EfficientNet B3 - weights ImageNet	lungs-baseline	0.629	0.845
EfficientNet B3 - weights None	lungs-nocrop-baseline	0.570	0.760

Metrics and results plots are spread over several notebooks:

• F1 Scores - f1 scores achieved by models on validation set
• Confusion Matrices - confusion matrices for each model configuration
• AUC Scores - AUC scores for all models configurations along with ROC and Precision-Recall curves for the best models

Explainability of the solution is taken up here:

• GradCAM - GradCAM analysis

Disclaimer

This project is intended for educational purposes only. It is not a substitute for professional medical advice, diagnosis or treatment.