Implementasi Metode Convolutional Neural Network untuk Klasifikasi Breast Cancer pada Citra Histopatologi - Muhammad Afrizal Amrustian; Merlinda Wibowo (JURNAL MEDIA INFORMATIKA BUDIDARMA, 2023)
Breast cancer is a tumor that manifests as an abnormal lump in the breast of a woman. The occurrence of breast cancer in women can be triggered by genetic and lifestyle factors. According to Global Cancer Statistics, 600,000 out of 2.3 million occurrences of breast cancer result in death. The death rate from breast cancer in Indonesia is likewise relatively high, reaching 17% for every 100,000 female inhabitants. Using histological pictures to diagnose breast cancer is one technique. The patient will capture an image of her breast cells, which will be examined and diagnosed by medical personnel. Even if histopathological scans are utilized as a baseline for the detection of breast cancer, the death rate associated with this disease remains rather high. One of the causes for the high mortality rate associated with breast cancer is the late detection of the disease, which results in patients being treated when the disease is in a severe state, and sometimes a misdiagnosis. The authors propose creating a breast cancer classification model utilizing the convolutional neural network (CNN) method in order to address the described issues. The study's findings indicate that CNN can classify breast cancer patients with an accuracy of 85 percent. Moreover, by calculating the loss function, the constructed model prevents overfitting.