Skin Cancer Detection Approach Using Convolutional Neural Network Artificial Intelligence

Sabda Norman Hayat, Lulu'ul Watef, Rarasmaya Indraswari


Skin cancer is a type of cancer that can cause death, where skin cancer is included in the 15 common cancers that occur in Indonesia. The number of skin cancer sufferers was around 6,170 cases of non-melanoma skin cancer and 1,392 cases of melanoma skin cancer in 2018 in Indonesia. Therefore, research related to skin cancer classification is increasing. This is done as an initial step in detecting whether a lesion can be said to be cancerous or not. The deep learning approach has certainly shown promising results in carrying out classification, so this research proposes a deep learning-based method used for skin cancer classification. The proposed approach involves Convolutional Neural Networks with the ISIC 2017 dataset. The models used for skin cancer classification are InceptionV3, EfficientNetB0, ResNet50, MobileNetV2, and NASNetMobile. The highest accuracy of the single model produced reached 69.3% using the MobileNetV2 model. An ensemble model combining the five models was also tested and produced the highest accuracy compared to other single models with an accuracy result of 80.6%.

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Skin Cancer, CNN, InceptionV3, EfficientNetB0, ResNet50, MobileNetV2 dan NASNetMobile

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IJIIS: International Journal of Informatics and Information Systems

ISSN:2579-7069 (Online)
Organized by:Departement of Information System, Universitas Amikom Purwokerto, IndonesiaFaculty of Computing and Information Science, Ain Shams University, Cairo, Egypt (publication issues) (managing editor) (technical & paper handling issues)

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