Klasifikasi Citra Histologi Kanker Payudara Menggunakan Metode Ensemble CNN
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Abstract
Breast cancer is a serious disease and the leading cause of death in women. Breast cancer can be diagnosed through medical imaging tests, such as radiological and hispathological images. However, because histological images have complexity and diversity, the manual examination has disadvantages, namely, requiring high expertise, time consuming, and prone to errors. Deep learning has been widely applied to histological classification including breast cancer, with automation to help overcome the shortcomings of manual diagnostic methods. In this study, Deep learning was developed using CNN with the ensemble method. The MobileNet, MobileNetV2 and VGG16 models were trained on the dataset and averaged the results of the two models. The experimental results show an increase in the ensemble compared to each model with an balanced accuracy 0.8689 dan F1-score 0.8682 in VGG16 + MobileNet combination, these results provide an increase compared to previous studies.
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