AAI_2025_Capstone_Chronicles_Combined

The model is compiled with the Adam optimizer (learning rate 1e-4), binary cross entropy loss, and metrics such as accuracy, AUC, precision, and recall. This architecture fitted well the task of medical diagnosis with both image and tabular data contributing to predictions.

Results/Conclusion

We evaluated the performance of both the EfficientNet-based model and the custom hybrid CNN using a validationset of chest radiographs labeled across seven consolidated diagnostic categories. Both approaches were designed to prioritize recall over precision, with the goal of minimizing false negatives in clinical decision support scenarios. This tradeoff reflected a core priority in medical imaging: missing a diagnosis, such as failing to flag a nodule or mass that could indicate early-stage lung cancer, can result in delayed intervention and significantly worsened outcomes. In contrast, a false positive is far less consequential, as it typically leads to additional review by a trained clinician rather than direct harm. As Oakden-Rayner (2020) explains, radiology AI systems should err on the side of caution by ensuring potentially abnormal cases are surfaced for review, even if some prove to be benign, because the clinical cost of a missed finding is far higher than the cost of an unnecessary follow-up. We believe this also blends well with ethical considerations for the AI since a human will always be the final review before a patient diagnosis.

EfficientNet Results

The EfficientNetB0 model was trained over five progressive phases, beginning with a one-epoch warm-up using binary cross-entropy with label smoothing. followed by 60 epochs of fine-tuning using focal loss. Gamma was initially set to 2.0 and later reduced to 1.5 to ease the penalization of confident predictions. Despite the extended training cycle (61 total epochs),

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