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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.18132277 |
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| _version_ | 1866901354917658624 |
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| author | Aditya Kumar Amita Mathews |
| author_facet | Aditya Kumar Amita Mathews |
| contents | <p><span>Breast cancer is a leading cause of cancer mortality among women globally, with disproportionately high late-stage detection rates in low-resource rural settings. This paper addresses the critical need for accessible, low-cost screening infrastructure in rural areas of Bihar, India, where geographical, economic, and socio-cultural barriers (including significant stigma) impede timely diagnosis. We propose a robust, two-tiered Machine Learning (ML) framework designed for Edge Computing deployment. The framework utilizes two models: 1) A Deep Convolutional Neural Network (CNN), fine-tuned using Transfer Learning on the public CBIS-DDSM dataset, for the binary classification of suspicious lesions identified in imaging/scanning data. 2) A Random Forest Classifier trained on structured clinical and demographic data to provide personalized, localized breast cancer risk prediction.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18132277 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A Cloud-Agnostic Machine Learning Framework for Mass-Level Early Breast Cancer Detection and Risk Prediction in Rural India Aditya Kumar Amita Mathews <p><span>Breast cancer is a leading cause of cancer mortality among women globally, with disproportionately high late-stage detection rates in low-resource rural settings. This paper addresses the critical need for accessible, low-cost screening infrastructure in rural areas of Bihar, India, where geographical, economic, and socio-cultural barriers (including significant stigma) impede timely diagnosis. We propose a robust, two-tiered Machine Learning (ML) framework designed for Edge Computing deployment. The framework utilizes two models: 1) A Deep Convolutional Neural Network (CNN), fine-tuned using Transfer Learning on the public CBIS-DDSM dataset, for the binary classification of suspicious lesions identified in imaging/scanning data. 2) A Random Forest Classifier trained on structured clinical and demographic data to provide personalized, localized breast cancer risk prediction.</span></p> |
| title | A Cloud-Agnostic Machine Learning Framework for Mass-Level Early Breast Cancer Detection and Risk Prediction in Rural India |
| url | https://doi.org/10.5281/zenodo.18132277 |