A systematic review of artificial intelligence-based image classification techniques for clinician diagnosis of skin cancer diseases

Shreyanth Chamakura; Deshao Liu; Ali A. Alwan; Rajesh Ampani; Oday A-Jerew; Abeer Alsadoon

doi:10.1007/978-3-031-71773-4_28

A systematic review of artificial intelligence-based image classification techniques for clinician diagnosis of skin cancer diseases

Shreyanth Chamakura, Deshao Liu, Ali A. Alwan, Rajesh Ampani, Oday A-Jerew, Abeer Alsadoon

School of Computer, Data and Mathematical Sciences

Research output: Chapter in Book / Conference Paper › Chapter › peer-review

Abstract

Skin cancer is a prevalent malignancy, and early detection is vital for effective treatment. However, visual examination of images for an accurate diagnosis is time-consuming and error-prone. Various computer-aided diagnosis methods have been developed to assist, but challenges persist in accurately identifying lesion features. This work aims to review AI-dependent lesion classification techniques for skin cancer prognosis. A systematic literature review was conducted to assess techniques, strengths, and limitations. Based on findings, a proposed system architecture with essential components is presented, offering a comprehensive understanding of skin lesion categorization techniques.

Original language	English
Title of host publication	Innovative Technologies in Intelligent Systems and Industrial Applications: CITISIA 2023
Editors	Subhas Chandra Mukhopadhyay, S. M. Namal Arosha Senanayake, P.W.C. Prasad
Place of Publication	Switzerland
Publisher	Springer
Pages	457-470
Number of pages	14
ISBN (Electronic)	9783031717734
ISBN (Print)	9783031717727
DOIs	https://doi.org/10.1007/978-3-031-71773-4_28
Publication status	Published - 2024
Event	International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications - Virtual, Online Duration: 14 Nov 2023 → 16 Nov 2023 Conference number: 8th

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	117 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications
Abbreviated title	CITISIA
City	Virtual, Online
Period	14/11/23 → 16/11/23

Keywords

Convolutional neural networks (CNN)
Deep learning
Lesions
Melanoma
Segmentation

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-031-71773-4_28

Cite this

Chamakura, S., Liu, D., Alwan, A. A., Ampani, R., A-Jerew, O., & Alsadoon, A. (2024). A systematic review of artificial intelligence-based image classification techniques for clinician diagnosis of skin cancer diseases. In S. C. Mukhopadhyay, S. M. N. A. Senanayake, & P. W. C. Prasad (Eds.), Innovative Technologies in Intelligent Systems and Industrial Applications: CITISIA 2023 (pp. 457-470). (Lecture Notes in Electrical Engineering; Vol. 117 LNEE). Springer. https://doi.org/10.1007/978-3-031-71773-4_28

Chamakura, Shreyanth ; Liu, Deshao ; Alwan, Ali A. et al. / A systematic review of artificial intelligence-based image classification techniques for clinician diagnosis of skin cancer diseases. Innovative Technologies in Intelligent Systems and Industrial Applications: CITISIA 2023. editor / Subhas Chandra Mukhopadhyay ; S. M. Namal Arosha Senanayake ; P.W.C. Prasad. Switzerland : Springer, 2024. pp. 457-470 (Lecture Notes in Electrical Engineering).

@inbook{00b688162cb5461b911b369e0fcf58d8,

title = "A systematic review of artificial intelligence-based image classification techniques for clinician diagnosis of skin cancer diseases",

abstract = "Skin cancer is a prevalent malignancy, and early detection is vital for effective treatment. However, visual examination of images for an accurate diagnosis is time-consuming and error-prone. Various computer-aided diagnosis methods have been developed to assist, but challenges persist in accurately identifying lesion features. This work aims to review AI-dependent lesion classification techniques for skin cancer prognosis. A systematic literature review was conducted to assess techniques, strengths, and limitations. Based on findings, a proposed system architecture with essential components is presented, offering a comprehensive understanding of skin lesion categorization techniques.",

keywords = "Convolutional neural networks (CNN), Deep learning, Lesions, Melanoma, Segmentation",

author = "Shreyanth Chamakura and Deshao Liu and Alwan, {Ali A.} and Rajesh Ampani and Oday A-Jerew and Abeer Alsadoon",

year = "2024",

doi = "10.1007/978-3-031-71773-4_28",

language = "English",

isbn = "9783031717727",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer",

pages = "457--470",

editor = "Mukhopadhyay, {Subhas Chandra} and Senanayake, {S. M. Namal Arosha} and P.W.C. Prasad",

booktitle = "Innovative Technologies in Intelligent Systems and Industrial Applications: CITISIA 2023",

note = "International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA ; Conference date: 14-11-2023 Through 16-11-2023",

}

Chamakura, S, Liu, D, Alwan, AA, Ampani, R, A-Jerew, O & Alsadoon, A 2024, A systematic review of artificial intelligence-based image classification techniques for clinician diagnosis of skin cancer diseases. in SC Mukhopadhyay, SMNA Senanayake & PWC Prasad (eds), Innovative Technologies in Intelligent Systems and Industrial Applications: CITISIA 2023. Lecture Notes in Electrical Engineering, vol. 117 LNEE, Springer, Switzerland, pp. 457-470, International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, Virtual, Online, 14/11/23. https://doi.org/10.1007/978-3-031-71773-4_28

A systematic review of artificial intelligence-based image classification techniques for clinician diagnosis of skin cancer diseases. / Chamakura, Shreyanth; Liu, Deshao; Alwan, Ali A. et al.
Innovative Technologies in Intelligent Systems and Industrial Applications: CITISIA 2023. ed. / Subhas Chandra Mukhopadhyay; S. M. Namal Arosha Senanayake; P.W.C. Prasad. Switzerland: Springer, 2024. p. 457-470 (Lecture Notes in Electrical Engineering; Vol. 117 LNEE).

Research output: Chapter in Book / Conference Paper › Chapter › peer-review

TY - CHAP

T1 - A systematic review of artificial intelligence-based image classification techniques for clinician diagnosis of skin cancer diseases

AU - Chamakura, Shreyanth

AU - Liu, Deshao

AU - Alwan, Ali A.

AU - Ampani, Rajesh

AU - A-Jerew, Oday

AU - Alsadoon, Abeer

N1 - Conference code: 8th

PY - 2024

Y1 - 2024

N2 - Skin cancer is a prevalent malignancy, and early detection is vital for effective treatment. However, visual examination of images for an accurate diagnosis is time-consuming and error-prone. Various computer-aided diagnosis methods have been developed to assist, but challenges persist in accurately identifying lesion features. This work aims to review AI-dependent lesion classification techniques for skin cancer prognosis. A systematic literature review was conducted to assess techniques, strengths, and limitations. Based on findings, a proposed system architecture with essential components is presented, offering a comprehensive understanding of skin lesion categorization techniques.

AB - Skin cancer is a prevalent malignancy, and early detection is vital for effective treatment. However, visual examination of images for an accurate diagnosis is time-consuming and error-prone. Various computer-aided diagnosis methods have been developed to assist, but challenges persist in accurately identifying lesion features. This work aims to review AI-dependent lesion classification techniques for skin cancer prognosis. A systematic literature review was conducted to assess techniques, strengths, and limitations. Based on findings, a proposed system architecture with essential components is presented, offering a comprehensive understanding of skin lesion categorization techniques.

KW - Convolutional neural networks (CNN)

KW - Deep learning

KW - Lesions

KW - Melanoma

KW - Segmentation

UR - http://www.scopus.com/inward/record.url?scp=85215688339&partnerID=8YFLogxK

UR - https://go.openathens.net/redirector/westernsydney.edu.au?url=https://doi.org/10.1007/978-3-031-71773-4_28

U2 - 10.1007/978-3-031-71773-4_28

DO - 10.1007/978-3-031-71773-4_28

M3 - Chapter

AN - SCOPUS:85215688339

SN - 9783031717727

T3 - Lecture Notes in Electrical Engineering

SP - 457

EP - 470

BT - Innovative Technologies in Intelligent Systems and Industrial Applications: CITISIA 2023

A2 - Mukhopadhyay, Subhas Chandra

A2 - Senanayake, S. M. Namal Arosha

A2 - Prasad, P.W.C.

PB - Springer

CY - Switzerland

T2 - International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications

Y2 - 14 November 2023 through 16 November 2023

ER -

Chamakura S, Liu D, Alwan AA, Ampani R, A-Jerew O, Alsadoon A. A systematic review of artificial intelligence-based image classification techniques for clinician diagnosis of skin cancer diseases. In Mukhopadhyay SC, Senanayake SMNA, Prasad PWC, editors, Innovative Technologies in Intelligent Systems and Industrial Applications: CITISIA 2023. Switzerland: Springer. 2024. p. 457-470. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-3-031-71773-4_28

A systematic review of artificial intelligence-based image classification techniques for clinician diagnosis of skin cancer diseases

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this