Automated Detection of Breast Cancer Histopathology Image Using Convolutional Neural Network and Transfer Learning

Didih Rizki Chandranegara (Universitas Muhammadiyah Malang, Malang, Indonesia)
Faras Haidar Pratama (Universitas Muhammadiyah Malang, Malang, Indonesia)
Sidiq Fajrianur (Universitas Muhammadiyah Malang, Malang, Indonesia)
Moch Rizky Eka Putra (Universitas Muhammadiyah Malang)
Zamah Sari (Universitas Muhammadiyah Malang, Malang, Indonesia)

MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer
Vol 22 No 3 (2023)

cancer caused 2.3 million cases and 685,000 deaths in 2020. Histopathology analysis is one of the tests used to determine a patient’s prognosis. However, histopathology analysis is a time-consuming and stressful process. With advances in deep learning methods, computer vision science can be used to detect cancer in medical images, which is expected to improve the accuracy of prognosis. This study aimed to apply Convolutional Neural Network (CNN) and Transfer Learning methods to classify breast cancer histopathology images to diagnose breast tumors. This method used CNN, Transfer Learning ((Visual Geometry Group (VGG16), and Residual Network (ResNet50)). These models undergo data augmentation and balancing techniques applied to undersampling techniques. The dataset used for this study was ”The BreakHis Database of microscopic biopsy images of breast tumors (benign and malignant),” with 1693 data classified into two categories: Benign and Malignant. The results of this study were based on recall, precision, and accuracy values. CNN accuracy was 94%, VGG16 accuracy was 88%, and ResNet50 accuracy was 72%. The conclusion was that the CNN method is recommended in detecting breast cancer to diagnose breast cancer.