Open AccessSkin Lesion Image Classification using Convolutional Neural Network
Author(s) -
Lailis Syafaah,
Ilham Hanami,
Inda Rusdia Sofiani,
Mohammad Chasrun
Publication year - 2021
Publication title -
kinetik
Language(s) - English
Resource type - Journals
eISSN - 2503-2267
pISSN - 2503-2259
DOI - 10.22219/kinetik.v6i4.1353
Subject(s) - skin cancer , melanoma , convolutional neural network , medicine , dermatology , skin lesion , melanocytic nevus , cancer , artificial intelligence , nevus , pattern recognition (psychology) , computer science , cancer research
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.