Explainable Deep Learning for Chest X-Ray Triage in Low-Resource Clinics

Abstract

A student capstone applying CNNs with Grad-CAM explanations to public chest X-ray datasets, packaged as an offline-capable triage assistant.

Methodology

The study follows a reproducible pipeline with publicly documented datasets and open-source tooling. Full experimental details, code and evaluation notebooks are available on request from the authors.

Key Findings

• Practical, low-cost approaches can match or exceed commercial baselines in the local context.
• Reproducibility and documentation significantly improve adoption by practitioners.
• Results are integrated into the ITA Academia curriculum as case studies.