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 & Mathematical Sciences

Charles Sturt University
Western Sydney University
Asia Pacific International College
The Institute of International Studies (TIIS)
Australian Institute of Higher Education
Ramapo College of New Jersey
Peninsula Health

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

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Convolutional neural networks (CNN)
Deep learning
Lesions
Melanoma
Segmentation

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).

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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",

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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",

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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 - https://www.scopus.com/pages/publications/85215688339

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

UN SDGs

Keywords

Access to Document

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