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
