Proceedings of the 11th International Applied Business and Engineering Conference, ABEC 2023, September 21st, 2023, Bengkalis, Riau, Indonesia

Research Article

Classification of Skin Cancer Using ResNet and VGG Deep Learning Network

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  • @INPROCEEDINGS{10.4108/eai.21-9-2023.2342881,
        author={Clara Lavita Angelina and Riki Umami Sanaz Ulfitria},
        title={Classification of Skin Cancer Using ResNet and VGG Deep Learning Network},
        proceedings={Proceedings of the 11th International Applied Business and Engineering Conference, ABEC 2023, September 21st, 2023, Bengkalis, Riau, Indonesia},
        publisher={EAI},
        proceedings_a={ABEC},
        year={2024},
        month={2},
        keywords={skin cancer; classification; deep learning; resnet; vgg; benign; malignant},
        doi={10.4108/eai.21-9-2023.2342881}
    }
    
  • Clara Lavita Angelina
    Riki Umami Sanaz Ulfitria
    Year: 2024
    Classification of Skin Cancer Using ResNet and VGG Deep Learning Network
    ABEC
    EAI
    DOI: 10.4108/eai.21-9-2023.2342881
Clara Lavita Angelina1,*, Riki Umami Sanaz Ulfitria1
  • 1: National Yunlin University of Science and Technology
*Contact email: d11010212@gemail.yuntech.edu.tw

Abstract

Skin cancer develops as a result of the unregulated growth of mutations in DNA caused by a variety of factors. Accurate classification of skin cancer is crucial to distinguishing high malignant potential from benign potential. In this paper, we utilized the ResNet-50, ResNet-101, VGG16, and VGG19 networks to develop a deep learning model for the classification of skin cancer. A total of 3,217 images from two types of skin cancer are used as a training dataset. Our method can automatically classify the feature type of skin cancer and record the prediction result in each image. The method has been evaluated on 80 test images. The experimental result shows that the ResNet50, ResNet101, VGG16, and VGG19 achieve accuracy up to 78.75%, 75%, 83.75%, and 73.75% respectively.