A Comprehensive Analysis of COVID-19 Detection Using Bangladeshi Data and Explainable AI

TL;DR

This paper enhances COVID-19 detection from chest X-ray images using machine learning, deep learning, and transfer learning models trained on Bangladeshi data, with explainable AI to improve transparency and reliability.

Contribution

It introduces a comprehensive analysis combining ML, DL, TL, and XAI techniques on Bangladeshi data for COVID-19 detection, highlighting the importance of explainability in medical imaging.

Findings

VGG19 achieved 98% accuracy in detection

LIME effectively explains model predictions

SMOTE improved class balance and model performance

Abstract

COVID-19 is a rapidly spreading and highly infectious virus which has triggered a global pandemic, profoundly affecting millions across the world. The pandemic has introduced unprecedented challenges in public health, economic stability, and societal structures, necessitating the implementation of extensive and multifaceted health interventions globally. It had a tremendous impact on Bangladesh by April 2024, with around 29,495 fatalities and more than 2 million confirmed cases. This study focuses on improving COVID-19 detection in CXR images by utilizing a dataset of 4,350 images from Bangladesh categorized into four classes: Normal, Lung-Opacity, COVID-19 and Viral-Pneumonia. ML, DL and TL models are employed with the VGG19 model achieving an impressive 98% accuracy. LIME is used to explain model predictions, highlighting the regions and features influencing classification decisions. SMOTE is applied to address class imbalances. By providing insight into both correct and incorrect classifications, the study emphasizes the importance of XAI in enhancing the transparency and reliability of models, ultimately improving the effectiveness of detection from CXR images.