Artificial intelligence-based image classification methods for diagnosis of skin cancer: Challenges and opportunities. (December 2020)

• Record Type: Journal Article
• Authors: Goyal, Manu; Knackstedt, Thomas; Yan, Shaofeng; Hassanpour, Saeed
• Is Part Of: Computers in biology and medicine. Volume 127(2020)
• Publication Date: 2020-12
• Subjects: Skin cancer -- Artificial intelligence -- Deep learning -- Dermatologists -- Computer-aided diagnostics -- Digital dermatology; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2020.104065
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Recently, there has been great interest in developing Artificial Intelligence (AI) enabled computer-aided diagnostics solutions for the diagnosis of skin cancer. With the increasing incidence of skin cancers, low awareness among a growing population, and a lack of adequate clinical expertise and services, there is an immediate need for AI systems to assist clinicians in this domain. A large number of skin lesion datasets are available publicly, and researchers have developed AI solutions, particularly deep learning algorithms, to distinguish malignant skin lesions from benign lesions in different image modalities such as dermoscopic, clinical, and histopathology images. Despite the various claims of AI systems achieving higher accuracy than dermatologists in the classification of different skin lesions, these AI systems are still in the very early stages of clinical application in terms of being ready to aid clinicians in the diagnosis of skin cancers. In this review, we discuss advancements in the digital image-based AI solutions for the diagnosis of skin cancer, along with some challenges and future opportunities to improve these AI systems to support dermatologists and enhance their ability to diagnose skin cancer. Highlights: The purpose of this review is to provide the reader with an update on the performance of artificial intelligence algorithms used for the diagnosis of skin cancer across various modalities of skin lesion datasets, especially in terms of the comparative studies on the performance of AI-based image classification algorithms and dermatologists/dermatopathologists. Different sub-sections are used to arrange these studies according to the types of imaging modality used, including clinical photographs, dermoscopy images, and whole-slide pathology scanning. Specifically, the technical challenges of these algorithms are discussed in the digital dermatology and opportunities to improve the current AI-based image classification solutions so that they can be used as a support tool for clinicians to enhance their efficiency in diagnosing skin cancers.