Open AccessBreast Cancer Detection in Mammography Image using Convolutional Neural Network
Author(s) -
Farrel Fahrozi,
Sugondo Hadiyoso,
Yuli Sun Hariyani
Publication year - 2022
Publication title -
jurnal rekayasa elektrika
Language(s) - English
Resource type - Journals
eISSN - 2252-620X
pISSN - 1412-4785
DOI - 10.17529/jre.v18i1.23255
Subject(s) - mammography , convolutional neural network , breast cancer , artificial intelligence , computer science , transfer of learning , pattern recognition (psychology) , cancer , artificial neural network , computer vision , medicine
Breast cancer is one of the non-contagious diseases that tends to increase every year. This disease occurs almost entirely in women, but can also occur in men. One way to detect this disease is by observing mammography images. However, mammography images often tend to be blurry with low quality so that it is possible to detect them incorrectly. Therefore, in this study, automatic classification of breast cancer on mammographic images was carried out using the Convolutional Neural Network (CNN). This proposed system uses the VGG16 architecture with a transfer learning system. The proposed system is then optimized using Adam optimizers and RMSprop optimizers. The results of system testing for normal, benign, and malignant classifications obtained an accuracy value of 80% - 90% with the highest accuracy achieved using Adam's optimizers. With this proposed system, it is hoped that it can help in the clinical diagnosis of breast cancer.