Taming Breast Cancer Diagnosis Through Transfer Learning: A Binary Classification Framework for Enhanced Disease Detection

Among the deadliest types of disease is cancer. It is also the most prevalent cancer in women, and if it is not correctly identified, it can even be fatal. Although medical technology has advanced significantly over the years, breast cancer detection remains a challenge that can only be solved through biopsy. By examining histological pictures seen under a microscope, pathologists can identify cancer. Visual inspection is an important part of cancer inspection; it takes time, competence, and a great deal of attention to detail. A quicker and more effective method of identifying breast cancer is therefore required. Numerous studies are being conducted to develop an effective partially or computer-monitored diagnostic system due to developments in deep learning and image analysis. To differentiate between benign and malignant forms of breast cancer, this study uses histopathological scans and convolutional neural networks in combination with transfer learning, a sophisticated kind of image-based machine learning. A large-scale multi-cancer dataset is used in the study's analysis. A refined VGG16 model that has been pre-trained for the categorization of breast cell images is presented in the article, exhibiting outstanding outcomes with an overall accuracy of 97.89 %, precision of 97 % and recall of 98 %. The incorporation of this model into healthcare systems has the potential to enable timely and precise diagnosis of breast cancer.