Computer-Aided Diagnosis Algorithm for Classification of Malignant Melanoma Using Deep Neural Networks

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 16; p. 5551
• Main Authors: Kim, Chan-Il, Hwang, Seok-Min, Park, Eun-Bin, Won, Chang-Hee, Lee, Jong-Ha
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 18.08.2021; MDPI
• Subjects: Algorithms; Artificial intelligence; computer aided diagnosis; Computers; convolutional neural network; Datasets; Deep learning; Diagnostic systems; Image classification; Machine learning; malanoma; Medical diagnosis; Medical imaging; Melanoma; Neural networks; Skin cancer; Tumors; South Korea
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21165551

Malignant melanoma accounts for about 1–3% of all malignancies in the West, especially in the United States. More than 9000 people die each year. In general, it is difficult to characterize a skin lesion from a photograph. In this paper, we propose a deep learning-based computer-aided diagnostic algorithm for the classification of malignant melanoma and benign skin tumors from RGB channel skin images. The proposed deep learning model constitutes a tumor lesion segmentation model and a classification model of malignant melanoma. First, U-Net was used to classify skin lesions in dermoscopy images. We implement an algorithm to classify malignant melanoma and benign tumors using skin lesion images and expert labeling results from convolutional neural networks. The U-Net model achieved a dice similarity coefficient of 81.1% compared to the expert labeling results. The classification accuracy of malignant melanoma reached 80.06%. As a result, the proposed AI algorithm is expected to be utilized as a computer-aided diagnostic algorithm to help early detection of malignant melanoma.