AAI_2025_Capstone_Chronicles_Combined

Introduction

Chest radiography is one of the most widely used tools in modern medicine for screening thoracic conditions. In this project, we developed two deep learning–based classifiers for NIH chest X-rays with the goal of assisting clinicians in identifying common lung diseases more efficiently. Our primary thesis was whether a convolutional neural network (CNN), trained on the NIH Chest X-ray dataset, could accurately detect multiple thoracic pathologies in new, unseen images. A successful system would need to deliver two core capabilities: (1) consistent, explainable classification of thoracic pathologies, and (2) visual tools that help clinicians interpret model predictions. We hypothesized that with sufficient data and careful preprocessing, CNN-based models could reach or even exceed benchmark performance on the NIH dataset for multi-label classification. The intended end users of this AI system include radiologists, medical specialists, and healthcare administrators. In a production setting, this model would process chest X-ray images from clinical workflows, return probabilistic predictions for each condition, and integrate with Picture Archiving and Communication Systems (PACS) or other imaging infrastructure. We ultimately built, tested and compared two distinct model architectures. The first was a hybrid custom CNN designed to incorporate both image and tabular data. The second was built around EfficientNet, a state-of-the-art CNN, pretrained on ImageNet (Tan & Le, 2019). Rather than

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