AI-Driven Insights in Breast Cancer: A Deep Learning Review Integrating Medical Imaging and Genomic

Hanoon, Rusul Hatem

doi:10.63463/kjes1209

	AI-Driven Insights in Breast Cancer: A Deep Learning Review Integrating Medical Imaging and Genomic
Kerbala Journal for Engineering Sciences (KJES)
Volume 6, Issue 1, March 2026, Pages 37-60 PDF (1.15 M)
Document Type: Review Article
DOI: 10.63463/kjes1209
Author
Rusul Hatem Hanoon^*
Department of Electrical and Electronic Engineering, Collage of Engineering, University of kerbala, Iraq.
Abstract
One of the main causes of cancer-related mortality among women globally is breast cancer. It presents a significant barrier to community public health, and better patient retrieval and intervention depend on early diagnosis and careful planning. Since breast cancer is one of the most significant or exclusive predictors of survival, machine learning and deep learning techniques have a great potential to increase the accuracy of both classification and identification. With a focus on visual data (mammograms) and molecular data (genomic biomarkers), this study offers a thorough summary of current developments in early breast cancer detection strategies. Strong techniques for learning from diverse data sources are provided by conventional deep learning models. However, its scalability, interpretability, and efficiency are dubious when applied to large-dimensional biological datasets. It has been suggested that topical research on explainable AI could improve the precision, openness, and dependability of early breast cancer diagnosis. By bypassing the black-box aspect of traditional AI systems, these technologies are intended to improve the slide and clinical credibility of figures on deep learning models. In order to speed up data analysis and improve prediction skills in healthcare imaging, researchers have also investigated well-established machine learning techniques. The development of AI-driven diagnostic tools that are more user-friendly and appropriate for practical medical applications is made possible by this advancement.
Keywords
CNN; deep learning; genetics; breast cancer; mammography; prognosis prediction

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