SHAP-Integrated Convolutional Diagnostic Networks for Feature-Selective Medical Analysis

Yan Hu; Ahmad Chaddad

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arXiv · 2503.08712

SHAP-Integrated Convolutional Diagnostic Networks for Feature-Selective Medical Analysis

Yan Hu·Ahmad Chaddad

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Abstract

This study introduces the SHAP-integrated convolutional diagnostic network (SICDN), an interpretable feature selection method designed for limited datasets, to address the challenge posed by data privacy regulations that restrict access to medical datasets. The SICDN model was tested on classification tasks using pneumonia and breast cancer datasets, demonstrating over 97% accuracy and surpassing four popular CNN models. We also integrated a historical weighted moving average technique to enhance feature selection. The SICDN shows potential in medical image prediction, with the code available on https://github.com/AIPMLab/SICDN.

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eess.IV cs.AI cs.CV cs.LG

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BibTeX RIS

Yan Hu, Ahmad Chaddad. 2025-03-10. SHAP-Integrated Convolutional Diagnostic Networks for Feature-Selective Medical Analysis. https://arxiv.org/abs/2503.08712

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