Investigating the detection of breast cancer with deep transfer learning using ResNet18 and ResNet34

• Published in: Biomedical physics & engineering express Vol. 10; no. 3; pp. 35029 - 35040
• Main Authors: Subaar, Christiana, Addai, Fosberg Tweneboah, Addison, Eric Clement Kotei, Christos, Olivia, Adom, Joseph, Owusu-Mensah, Martin, Appiah-Agyei, Nelson, Abbey, Shadrack
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 01.05.2024
• Subjects: breast; cancer; mammography; ResNet18; ResNet34; Breast; ResNet18; ResNet34; mammography; Cancer
• ISSN: 2057-1976; 2057-1976
• DOI: 10.1088/2057-1976/ad3cdf

A lot of underdeveloped nations, particularly in Africa, struggle with cancer-related, deadly diseases. Particularly in women, the incidence of breast cancer is rising daily because of ignorance and delayed diagnosis. Only by correctly identifying and diagnosing cancer in its very early stages of development can be effectively treated. The classification of cancer can be accelerated and automated with the aid of computer-aided diagnosis and medical image analysis techniques. This research provides the use of transfer learning from a Residual Network 18 (ResNet18) and Residual Network 34 (Resnet34) architectures to detect breast cancer. The study examined how breast cancer can be identified in breast mammography pictures using transfer learning from ResNet18 and ResNet34, and developed a demo app for radiologists using the trained models with the best validation accuracy. For the multi-class classification of the images, the study gave an average accuracy for binary classification of benign or malignant cancer cases of 86.7% validation accuracy for ResNet34 and 92% validation accuracy for ResNet18. A prototype web application showcasing ResNet18 performance has been created. The acquired results show how transfer learning can improve the accuracy of breast cancer detection, providing invaluable assistance to medical professionals, particularly in an African scenario.&#xD.