Computer assisted diagnosis of skin cancer: A survey and future recommendations. (December 2022)

• Record Type: Journal Article
• Authors: Khattar, Sonam; Kaur, Ravinder
• Is Part Of: Computers & electrical engineering. Volume 104:Part B(2022)
• Publication Date: 2022-12
• Subjects: Skin cancer -- Survey -- Deep learning -- Skin lesions -- Pre-processing -- Segmentation -- Classification; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108431
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Clinical screening of skin cancer using images is tedious and laborious task. Thus, Computer-Aided-System (CAD) can assist to perform early cancer diagnosis. This paper provides critical analysis of existing CAD systems for skin lesions. The distinct research challenges for diagnosis of skin lesions are also elaborated. Abstract: Skin cancer is amid the most frequent types of cancer, accounting for approximately 2 to 3 million cases being diagnosed each year worldwide. Abnormal cell development on the skin causes skin lesions and manual inspection of skin lesions is a difficult, challenging, instinctive, and tedious task. Computer Aided Diagnosis (CAD) techniques can assist doctors to enhance their investigation skills and reduce the time it takes to get a precise diagnosis. Furthermore, the lack of advanced, user-friendly CAD techniques has raised serious concerns about the noninvasive, precise, and rapid identification of diseases. CAD systems can help to make an early diagnosis of skin lesions to plan timely treatment schedules for the patients to increase their survival rates. However, due to the distinctive and complex properties of skin lesion images, examination of skin lesion images still poses significant difficulties. The motivation behind this study is to discuss several preprocessing, segmentation, and classification strategies for analyzing skin lesions to differentiate between cancerous and non-cancerous images. The primary goal is to provide an overview for naïve researchers to commence their research in this field. Moreover, this manuscript will also highlight open challenges and future recommendations which further calls the distinct researchers to begin their research in this domain. Graphical abstract: Image, graphical abstract