Optimizing CNN Architectures for Advanced Thoracic Disease Classification

2025/02/15 by Tejas Mirthipati, Mirthipati, Tejas
Health Professions · Medicine · Neuroscience · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Healthcare #Brain Tumor Detection and Classification #COVID-19 diagnosis using AI #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.2502.10614

openalex publication_date 2025/02/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Machine learning, particularly convolutional neural networks (CNNs), has shown promise in medical image analysis, especially for thoracic disease detection using chest X-ray images. In this study, we evaluate various CNN architectures, including binary classification, multi-label classification, and ResNet50 models, to address challenges like dataset imbalance, variations in image quality, and hidden biases. We introduce advanced preprocessing techniques such as principal component analysis (PCA) for image compression and propose a novel class-weighted loss function to mitigate imbalance issues. Our results highlight the potential of CNNs in medical imaging but emphasize that issues like unbalanced datasets and variations in image acquisition methods must be addressed for optimal model performance.

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