Deteksi Otomatis Citra Histopatologi Kanker Payudara Menggunakan VGG 16

Breast cancer is the most frequent type of cancer in women and the leading cause of death globally. In 2020, there were 2.3 million new cases of breast cancer and 685,000 deaths. Histopathology analysis is one of the techniques performed to determine a patient's prognosis.. Histopathology analysis, on the other hand, is a time-consuming and difficult process. Deep learning breakthroughs are allowing computer vision research to spot cancer in medical photographs, which is predicted to increase prognosis efficiency. This target of study to apply the Transfer Learning VGG16 method to categorize breast cancer Breast tumors are diagnosed using histopathology pictures. This method uses Transfer Learning Visual Geometry Group (VGG16) with an accuracy of 88%. These models employ data augmentation and balancing strategies in conjunction with undersampling techniques. The dataset used for this study is "BreakHis-400X Database of microscopic biopsy images of breast tumors (benign and malignant)," Divided into two classes: malignant and benign which contains 1693 data points. This Research’s findings are based on accuracy, recall, and precision values. The conclusion is that the VGG16 technique is suggested for identifying and diagnosing breast cancer.