Classification and Gradient-based

Contents

• Introduction
• Overview
• Steps
• Results
• Usage
• Conclusions
• References

Introduction

A team of radiologists from New Orleans studied the usefulness of Chest Radiographs for diagnosing COVID-19 compared to the reverse-transcription polymerase chain reaction (RT-PCR) and found out they could aid rapid diagnosis, especially in areas with limited testing facilities [1].
Another study found out that the radiographs of different viral cases of pneumonia are comparative, and they overlap with other infectious and inflammatory lung diseases, making it hard for radiologists to recognize COVID‐19 from other viral pneumonia cases [2].
This project aims to make the former study a reality while dealing with the intricacies in the latter, with the help of Deep Learning.

Overview

The project uses the COVID-19 Radiography Database [3] as it's dataset. It has a total of 21165 Chest X-Rays (CXRs) belonging to 4 different classes (COVID-19, Lung Opacity, Normal and Viral Pneumonia).
Three top scoring CNN architectures, VGG-16 [4], ResNet-18 [5] and DenseNet-121 [6], trained on the ImageNet Dataset [7], were chosen for fine-tuning on the dataset.
The results obtained from the different architectures were then evaluted and compared.
Finally, with the help of Gradient weighted Class Activation Maps (Grad-CAM) [8] the affected areas in CXRs were localized.

• Note: The dataset and the trained models can be found in here.
  

Steps

1. Dataset Exploration
2. Split the dataset

   Type	COVID-19	Lung Opacity	Normal	Viral Pneumonia	Total
   Train	3496	5892	10072	1225	20685
   Val	60	60	60	60	240
   Test	60	60	60	60	240

3. Fine-tune VGG-16, ResNet-18 and DenseNet-121
   1. Define Transformations
   2. Handle imbalanced dataset with Weighted Random Sampling (Over-sampling)
   3. Prepare the Pre-trained models
   4. Fine-tune step with Early-stopping

      • Hyper-parameters
        Learning rate	0.00003
        Batch Size	32
        Number of Epochs	25

      • Loss Function	Optimizer
        Categorical Cross Entropy	Adam

   5. Plot running losses & accuracies

      • Model	Summary Plot
        VGG-16
        ResNet-18
        DenseNet-121

4. Results Evaluation
   1. Plot confusion matrices
   2. Compute test-set Accuracy, Precision, Recall & F1-score
   3. Localize using Grad-CAM
5. Inference

Results

	VGG-16	ResNet-18	DenseNet-121
Pathology COVID-19 Lung Opacity Normal Viral Pneumonia	Accuracy Precision Recall F1-Score 0.9956 0.9833 1.0000 0.9916 0.9582 0.8833 0.9464 0.9138 0.9622 0.9667 0.8923 0.9280 0.9913 0.9833 0.9833 0.9833	Accuracy Precision Recall F1-Score 0.9871 0.9667 0.9830 0.9748 0.9664 0.8667 1.0000 0.9286 0.9664 1.0000 0.8823 0.9375 0.9957 1.0000 0.9836 0.9917	Accuracy Precision Recall F1-Score 0.9957 0.9833 1.0000 0.9916 0.9623 0.9167 0.9322 0.9244 0.9623 0.9500 0.9047 0.9268 0.9957 0.9833 1.0000 0.9916
TL;DR Train set Test set	Total Correct Predictions Total Accuracy 20362 98.44% 229 95.42%	Total Correct Predictions Total Accuracy 20639 99.78% 230 95.83%	Total Correct Predictions Total Accuracy 20540 99.30% 230 95.83%
Confusion Matrices

• Localization with Gradient-based Class Activation Maps

COVID-19 infected CXR	VGG-16	ResNet-18	DenseNet-121