Authors: Kouhei Shimizu; Hitoshi Iyatomi; Kerri-Ann Norton; M. Emre Celebi
Addresses: Hosei University, Koganai Campus, 3-7-2, KajioChou, Koganei City, Tokyo, 184-8584, Japan ' Hosei University, Koganai Campus, 3-7-2, KajioChou, Koganei City, Tokyo, 184-8584, Japan ' Johns Hopkins University, 614 Traylor Research Building, 720 Rutland Ave., Baltimore, MD 21201, USA ' Louisiana State University in Shreveport, Technology Center 206, One University Place Shreveport, LA 71115, USA
Abstract: In this paper, we present an automated melanoma screening system that supports not only melanocytic skin lesions (MSLs) but also non-melanocytic skin lesions (NoMSLs). Melanoma is known as the most fatal skin cancer. Therefore, early detection is highly desired. However, melanoma diagnosis is not easy even for expert dermatologists. Several researchers have developed automated methods for melanoma detection but they mostly focused only on MSLs while NoMSLs have been almost neglected. To expand the scope to NoMSLs, we developed two melanoma classification models, namely the single-shot model and the double-shot model. The single-shot model differentiates melanomas from all the other skin lesions including NoMSLs. The double-shot model divides the task into two subtasks. Firstly, it differentiates MSLs from NoMSLs and then differentiates melanomas from the other MSLs. The single-shot achieved a sensitivity (SE) of 92.9% and a specificity (SP) of 83.9%, while the double-shot achieved an SE of 97.6% and an SP of 87.7% with ten image features. Through a detailed data analysis, we confirmed that the double-shot can exploit effective image features more than the single-shot as a result of its task decomposition method.
Keywords: computer-aided diagnosis; dermoscopy; melanoma screening; medical images; automated screening; non-melanocytic skin lesions; skin cancer; melanoma classification models; image features; task decomposition.
International Journal of Computer Applications in Technology, 2014 Vol.50 No.1/2, pp.122 - 130
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 25 Jul 2014 *