SHAP-Integrated Convolutional Diagnostic Networks for Feature-Selective Medical Analysis
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910870391488512 |
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| author | Hu, Yan Chaddad, Ahmad |
| author_facet | Hu, Yan Chaddad, Ahmad |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08712 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SHAP-Integrated Convolutional Diagnostic Networks for Feature-Selective Medical Analysis Hu, Yan Chaddad, Ahmad Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning 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. |
| title | SHAP-Integrated Convolutional Diagnostic Networks for Feature-Selective Medical Analysis |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2503.08712 |