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Vol. 6, No. 4, November 2021

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Skin Lesion Image Classification using Convolutional Neural Network

https://doi.org/10.22219/kinetik.v6i4.1353
Lailis Syafa'ah
Universitas Muhammadiyah Malang
Ilham Hanami
Universitas Muhammadiyah Malang
Inda Rusdia Sofiani
Universitas Muhammadiyah Malang
Mohammad Chasrun
Universitas Muhammadiyah Malang

Corresponding Author(s) : Lailis Syafa'ah

lailis@umm.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 6, No. 4, November 2021

Abstract

Classification of skin cancer is an important task to detect skin cancer and help with the treatment of skin cancer according to its type. There are many techniques in imaging used to classify skin cancer, one of the superior deep learning (DL) algorithms for classification is the Convolutional Neural Network (CNN). One type of skin cancer is dangerous is melanoma. In this study, CNN is proposed to help classify this type of skin cancer. The dataset consists of 15103 images of skin cancer pigments with 7 different types of skin cancer. These three tests proved malignant skin lesions can be classified with higher accuracy than non-melanocytic skin lesions which is 90% and performance evaluation shows melanocytic and non-melanocytic skin lesions detected with the highest accuracy. The tests conducted in this study grouped several types of skin diseases namely the first tests conducted using a group of melanocytic and non-melanocytic skin disease, second testing using groups of melanoma and melanocytic nevus diseases, and the final testing using malignant and benign. The proposed CNN model achieved significant performance with a best accuracy of 94% on the classification of melanoma and melanocytic nevus.

Keywords

CNN Deep Learning Dataset Skin cancer
Syafa’ah, L., Hanami, I., Rusdia Sofiani, I., & Chasrun, M. (2021). Skin Lesion Image Classification using Convolutional Neural Network. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 6(4). https://doi.org/10.22219/kinetik.v6i4.1353
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