AAI_2025_Capstone_Chronicles_Combined

prioritize sensitivity, especially in medical imaging tasks where catching all possible positives is more valuable than reducing false alarms.

Hybrid CNN Results

The hybrid CNN model, which fused grayscale chest X-ray images with tabular metadata (e.g., patient age and gender), was trained using both a multi-task architecture and a collection of seven single-task classifiers. Each model targeted one of the consolidated diagnostic categories, and training was guided by class-balanced data splits to mitigate label imbalance. AUC scores above 0.5 and steadily decreasing loss curves (see Figure 5) confirmed that the models were learning meaningful patterns rather than memorizing noise. The single-task classifier combination consistently outperformed the multi-task approach in terms of accuracy, recall, and overall stability. This outcome suggests that training independent models allowed each classifier to specialize more effectively in its respective task. In contrast, the multi-task model appeared to struggle with shared representation learning, showing slower and noisier convergence (see Figure 6).

Fig.6 Lung Structure Issues classifier's loss

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