Application of Deep Learning with ResNet50 for Early Detection of Melanoma Skin Cancer

Abstract

Cancer is a type of disease that can be fatal. Some of the cancers with the highest death rates in Indonesia include uterine cancer, breast cancer and skin cancer. The most malignant types of skin cancer are melanoma, which has a high mortality rate, especially if not detected in the early stages, and non-melanoma skin cancer (NMS Cs). Management of this disease depends on whether the type of skin cancer is malignant (malignant) or non-malignant (benign). Therefore, we need a system that can classify types of skin cancer with high accuracy. In this research, the author will use deep learning with the InceptionV3 and ResNet50 algorithms to carry out classification. The aim of this research is to classify types of skin cancer using the InceptionV3 and ResNet50 architecture. The skin cancer dataset used consists of two classes, namely Benign and Malignant, with a total of 3297 data, consisting of 660 data for testing and 2637 data for training. Research stages include data acquisition, preprocessing, classification, and analysis of results. Experimental results show that ResNet-50 produces the best performance with an accuracy level of 0.87. Innovations from this research include using a larger dataset, testing two deep learning architectures, modifying hyperparameters, and using a different layer architecture, which produces better accuracy than previous research. It is hoped that the results of this research can be applied to classify skin cancer more accurately.