Role of Artificial Intelligence and Deep Learning in Skin Disease Prediction: A Systematic Review and Meta-analysis

V. Auxilia Osvin Nancy (), P. Prabhavathy and Meenakshi S. Arya
Additional contact information
V. Auxilia Osvin Nancy: SRM Institute of Science and Technology
P. Prabhavathy: SRM Institute of Science and Technology
Meenakshi S. Arya: IOWA State University

Annals of Data Science, 2024, vol. 11, issue 6, No 11, 2109-2139

Abstract: Abstract Skin is a most essential and extraordinary part of the human structure. Exposure to chemicals such as nitrates, sunlight, arsenic, and UV rays due to pollution and depletion of the ozone layer is causing various skin diseases to spread rapidly. Digital healthcare offers many opportunities to reduce time, and human error, and improve clinical outcomes. However, the automatic recognition of skin disease is a major challenge due to high visual similarity between different skin diseases, low contrast, and large inter variation. Early detection of skin cancer can prevent death. Thus, Artificial intelligence (AI) and Machine Learning (ML) helps the physicians to improve clinical judgment or change manual perception. For skin cancer diagnostics, the ML/AI algorithm can outperform or match professional dermatologists in multiple studies. Different pre-trained architectures such as ResNet152, AlexNet, VGGNet, etc. are used for fusing different skin disease features such as texture, color, etc. and they are also utilized for conducting segmentation tasks. The variations in reflection, lesion size, shape, illumination, etc. often make automatic skin disease classification a complex task. ISIC 2019 and HAM 10000 are the widely used public datasets for skin disease prediction. More technical paper on skin cancer diagnosis is compared in this study. This report examines the majority of technical papers published between 2018 and October 2022 in order to appreciate current trends in the disciplines of skin cancer prediction. A study that combined clinical patient data with deep learning models (DL) increased the accuracy of predicting skin cancer. This article presents a visually attractive and well-organized summary of the current study findings.

Keywords: CNN; Deep learning; Skin cancer; HAM10000; ISIC2019; Augmentation (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s40745-023-00503-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:aodasc:v:11:y:2024:i:6:d:10.1007_s40745-023-00503-2

Ordering information: This journal article can be ordered from
https://www.springer ... gement/journal/40745

DOI: 10.1007/s40745-023-00503-2

Access Statistics for this article

Annals of Data Science is currently edited by Yong Shi

More articles in Annals of Data Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().