A DEEP LEARNING APPROACH FOR CLASSIFICATION OF SIX CASES OF CHEST X-RAYS

Abstract

Artificial Intelligence (AI), particularly deep learning, is transforming healthcare by enabling automated analysis and diagnosis from medical images, addressing critical challenges such as the shortage of radiologists and the demand for accurate diagnostic systems. This study focuses on the classification of six distinct chest X-ray conditions: Normal, Pneumonia, Tuberculosis, Lung Mass, Rib Fracture, and Enlarged Heart. Using a dataset of 10,200 chest X-ray images collected from Hargeisa Group Hospital, two pretrained convolutional neural network (CNN) architectures, DenseNet and GoogleNet, were fine-tuned and evaluated. Comprehensive preprocessing, including noise removal, image enhancement, and augmentation techniques, ensured high-quality and balanced training data. The models demonstrated exceptional performance, achieving classification accuracies of 97% and 96%, respectively, surpassing benchmarks in multi-class medical image classification Despite these promising results, the study encountered limitations. The dataset size, while sufficient for this research, remains relatively small for broader generalizability. Additionally, processing sensitive personal data required compliance with Somaliland’s Data Protection Act, posing challenges in accessing and utilizing X-ray images. These limitations highlight the need for expanded datasets and improved data access protocols for future research.This research establishes a robust framework for automating chest X-ray diagnostics, empowering radiologists with timely and accurate decision support. The findings contribute to advancing AIdriven solutions for healthcare, addressing both global and region-specific challenges