Artificial Intelligence for Skin Cancer Detection: Scoping Review.
Background: Skin cancer is the most common cancer type affecting humans. Traditional skin cancer diagnosis methods are costly, require a professional physician, and take time. Hence, to aid in diagnosing skin cancer, artificial intelligence (AI) tools are being used, including shallow and deep machi...
| Published in: | Journal of Medical Internet Research Vol. 23; no. 11; pp. 1 - 18 |
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| Main Authors: | , , , , |
| Format: | research Journal Article |
| Published: |
JMIR Publications Inc.
Nov2021
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=153881977&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153881977 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14394456 DNC jtl: Journal of Medical Internet Research issn: 14394456 maglogo: N pubinfo: dt: Nov2021 vid: 23 iid: 11 pid: 21567 pub: JMIR Publications Inc. place: Toronto, Ontario artinfo: ui: 153881977 153881977 NLM34821566 153881977 10.2196/22934 NLM34821566 153881977 ppf: 1 ppct: 17 formats: tig: atl: Artificial Intelligence for Skin Cancer Detection: Scoping Review. aug: au: Takiddin, Abdulrahman Schneider, Jens Yang, Yin Abd-Alrazaq, Alaa Househ, Mowafa affil: 1 Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, United States sug: subj: Artificial Intelligence Skin Neoplasms Diagnosis Algorithms Human Reproducibility of Results ab: Background: Skin cancer is the most common cancer type affecting humans. Traditional skin cancer diagnosis methods are costly, require a professional physician, and take time. Hence, to aid in diagnosing skin cancer, artificial intelligence (AI) tools are being used, including shallow and deep machine learning-based methodologies that are trained to detect and classify skin cancer using computer algorithms and deep neural networks.Objective: The aim of this study was to identify and group the different types of AI-based technologies used to detect and classify skin cancer. The study also examined the reliability of the selected papers by studying the correlation between the data set size and the number of diagnostic classes with the performance metrics used to evaluate the models.Methods: We conducted a systematic search for papers using Institute of Electrical and Electronics Engineers (IEEE) Xplore, Association for Computing Machinery Digital Library (ACM DL), and Ovid MEDLINE databases following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. The studies included in this scoping review had to fulfill several selection criteria: being specifically about skin cancer, detecting or classifying skin cancer, and using AI technologies. Study selection and data extraction were independently conducted by two reviewers. Extracted data were narratively synthesized, where studies were grouped based on the diagnostic AI techniques and their evaluation metrics.Results: We retrieved 906 papers from the 3 databases, of which 53 were eligible for this review. Shallow AI-based techniques were used in 14 studies, and deep AI-based techniques were used in 39 studies. The studies used up to 11 evaluation metrics to assess the proposed models, where 39 studies used accuracy as the primary evaluation metric. Overall, studies that used smaller data sets reported higher accuracy.Conclusions: This paper examined multiple AI-based skin cancer detection models. However, a direct comparison between methods was hindered by the varied use of different evaluation metrics and image types. Performance scores were affected by factors such as data set size, number of diagnostic classes, and techniques. Hence, the reliability of shallow and deep models with higher accuracy scores was questionable since they were trained and tested on relatively small data sets of a few diagnostic classes. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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