AAI_2025_Capstone_Chronicles_Combined

and fewer parameters. Studies like those by Kufel et al. (2023) and Nawaz et al. (2023) have shown that EfficientNet variants deliver competitive or superior results in multi-label chest X-ray classification tasks. Previous projects like CheXNet (Rajpurkar et al., 2018) and ChestNet (Pham et al., 2020) have demonstrated that DenseNet-121–based architectures can also achieve high AUCs (~0.80–0.82) for many thoracic conditions. Our work builds on these foundations by applying more modern architectures and by explicitly modeling multi-label correlations; recognizing that real-world chest radiographs often present multiple co-occurring pathologies. In addition, this dual-model approach also allowed us to explore the trade-offs between handcrafted feature inclusion (in the hybrid model) and state-of-the-art image-based generalization (in EfficientNet). Together, these strategies reflect current best practices in academic and applied machine learning for radiographic classification. This project implemented and compared two distinct machine learning pipelines for multi-label classification of chest pathologies. The first was a fine-tuned EfficientNetB0 model leveraging transfer learning. The second was a custom-built convolutional neural network (CNN) that incorporated both image and tabular metadata using a hybrid architecture. Both models were trained on the same curated version of the NIH Chest X-ray dataset, using the exact same initial preprocessing steps and seven consolidated diagnostic labels. Experimental Methods

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