Framework for Detecting Breast Cancer Risk Presence Using Deep Learning

• Published in: Electronics (Basel) 2023-01, Vol.12 (2), p.403
• Main Authors: Humayun, Mamoona, Khalil, Muhammad Ibrahim, Almuayqil, Saleh Naif, Jhanjhi, N. Z.
• Format: Article
• Language: English
• Subjects: Accuracy; Biomarkers; Breast cancer; Classification; Decisions; Deep learning; Digitization; Gene expression; Histopathology; Imaging techniques; Indicators; Machine learning; Mammography; Medical imaging; Medical prognosis; Medical research; Model accuracy; Patients; Public health; Risk assessment
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics12020403

Cancer is a complicated global health concern with a significant fatality rate. Breast cancer is among the leading causes of mortality each year. Advancements in prognoses have been progressively based primarily on the expression of genes, offering insight into robust and appropriate healthcare decisions, owing to the fast growth of advanced throughput sequencing techniques and the use of various deep learning approaches that have arisen in the past few years. Diagnostic-imaging disease indicators such as breast density and tissue texture are widely used by physicians and automated technology. The effective and specific identification of cancer risk presence can be used to inform tailored screening and preventive decisions. For several classifications and prediction applications, such as breast imaging, deep learning has increasingly emerged as an effective method. We present a deep learning model approach for predicting breast cancer risk primarily on this foundation. The proposed methodology is based on transfer learning using the InceptionResNetV2 deep learning model. Our experimental work on a breast cancer dataset demonstrates high model performance, with 91% accuracy. The proposed model includes risk markers that are used to improve breast cancer risk assessment scores and presents promising results compared to existing approaches. Deep learning models include risk markers that are used to improve accuracy scores. This article depicts breast cancer risk indicators, defines the proper usage, features, and limits of each risk forecasting model, and examines the increasing role of deep learning (DL) in risk detection. The proposed model could potentially be used to automate various types of medical imaging techniques.