Chest Disease Detection In X-Ray Images Using Deep Learning Classification Method

TL;DR

This paper explores the use of transfer learning with CNNs to classify chest X-ray images into COVID-19, pneumonia, TB, and normal, achieving high accuracy and interpretability with Grad-CAM.

Contribution

It evaluates multiple CNN models with transfer learning for multi-class chest X-ray classification and incorporates Grad-CAM for interpretability.

Findings

High accuracy in classifying chest X-ray images.

Effective use of Grad-CAM for model interpretability.

Strong performance metrics across categories.

Abstract

In this work, we investigate the performance across multiple classification models to classify chest X-ray images into four categories of COVID-19, pneumonia, tuberculosis (TB), and normal cases. We leveraged transfer learning techniques with state-of-the-art pre-trained Convolutional Neural Networks (CNNs) models. We fine-tuned these pre-trained architectures on a labeled medical x-ray images. The initial results are promising with high accuracy and strong performance in key classification metrics such as precision, recall, and F1 score. We applied Gradient-weighted Class Activation Mapping (Grad-CAM) for model interpretability to provide visual explanations for classification decisions, improving trust and transparency in clinical applications.