Artificial Intelligence Framework for Skin Lesion Prediction Using Medical Dermoscopic Images - Abualkebash, Humam; Saleh, Radhwan A. A.; Ertunc, H. Metin; Addo, Daniel; Talo, Muhammed; Al-Antari, Mugahed A. (Institute of Electrical and Electronics Engineers Inc., 2023)
Melanoma, recognized as the most lethal form of cancer, originates from the uncontrolled proliferation of melanocytes, cells responsible for melanin production. Identifying melanoma lesions poses a formidable challenge due to their visual resemblance to non-cancerous lesions. This paper presents an end-to-end skin lesion prediction framework that includes data collection, preprocessing, data augmentation, the state-of-the-art YOLOv8 prediction model, and evaluation procedure. To train and assess the proposed framework, two benchmark medical datasets were collected and used: (1) PH2 and (2) ISIC2018. The preprocessing procedure is conducted to improve prediction performance, and the augmentation process is used to increase the training set size to meet the requirements of the AI model. Subsequently, the state-of-the-art deep learning YOLOv8 model is adopted and fine-tuned as the core prediction component for the proposed framework. The detection results achieved an overall mAPs@0.5 of 99.50% and 97.30% using the PH2 and ISIC2018 datasets, respectively. These promising and encouraging evaluation results appear to be practically useful for establishing an end-to-end prediction framework for skin lesions. © 2023 IEEE.
7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023 -- 26 October 2023 through 28 October 2023 -- Ankara -- 194332
Keywords: Artificial Intelligence; Melanoma Detection; Skin Lesion; YOLO