Nur Mazshuky Mazelan@mazshuky859

This project implements a Clinical Decision Support (CDS) API for automated chest X-ray interpretation. Built on a pretrained DenseNet-169 model fine-tuned with the ChestX-ray14 dataset, it detects 14 common thoracic conditions plus “No Finding.” Grad-CAM is integrated to generate heatmaps, allowing developers and clinicians to visualise the regions that most influenced predictions. To bridge the gap between raw model output and clinical usability, GPT-5 is used to generate human-readable reports, configurable for either clinicians (technical, diagnostic detail) or patients (simplified explanations). The API is built with FastAPI and exposes endpoints for prediction, Grad-CAM overlay retrieval, and report generation. Key benefits include explainable predictions, modular design for integration into research or healthcare applications, and support for multimodal workflows combining imaging AI with natural language generation.

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