Next Article in Journal
Can FinTech Applied to Payments Improve Consumer Financial Satisfaction? Evidence from the USA
Next Article in Special Issue
Diagnosing Vascular Aging Based on Macro and Micronutrients Using Ensemble Machine Learning
Previous Article in Journal
Improving the Performance of RODNet for MMW Radar Target Detection in Dense Pedestrian Scene
Previous Article in Special Issue
DExMA: An R Package for Performing Gene Expression Meta-Analysis with Missing Genes
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Mathematics
Volume 11
Issue 2
10.3390/math11020364
mathematics-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

DeepLabv3+-Based Segmentation and Best Features Selection Using Slime Mould Algorithm for Multi-Class Skin Lesion Classification

by
Mehwish Zafar
1,
Javeria Amin
2,*,
Muhammad Sharif
1,
Muhammad Almas Anjum
3,
Ghulam Ali Mallah
4 and
Seifedine Kadry
5,6,7
1
Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47040, Pakistan
2
Department of Computer Science, University of Wah, Wah Cantt 47040, Pakistan
3
National University of Technology (NUTECH), Islamabad 44000, Pakistan
4
Department of Computer Science, Shah Abdul Latif University, Khairpur 66111, Pakistan
5
Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway
6
Artificial Intelligence Research Center (AIRC), Ajman University, Ajman P.O. Box 346, United Arab Emirates
7
Department of Electrical and Computer Engineering, Lebanese American University, Byblos P.O. Box 13-5053, Lebanon
*
Author to whom correspondence should be addressed.
Mathematics 2023, 11(2), 364; https://doi.org/10.3390/math11020364
Submission received: 18 September 2022 / Revised: 28 December 2022 / Accepted: 4 January 2023 / Published: 10 January 2023
(This article belongs to the Special Issue Current Research in Biostatistics)
Browse Figures
Versions Notes

Abstract

The development of abnormal cell growth is caused by different pathological alterations and some genetic disorders. This alteration in skin cells is very dangerous and life-threatening, and its timely identification is very essential for better treatment and safe cure. Therefore, in the present article, an approach is proposed for skin lesions’ segmentation and classification. So, in the proposed segmentation framework, pre-trained Mobilenetv2 is utilised in the act of the back pillar of the DeepLabv3+ model and trained on the optimum parameters that provide significant improvement for infected skin lesions’ segmentation. The multi-classification of the skin lesions is carried out through feature extraction from pre-trained DesneNet201 with N × 1000 dimension, out of which informative features are picked from the Slim Mould Algorithm (SMA) and input to SVM and KNN classifiers. The proposed method provided a mean ROC of 0.95 ± 0.03 on MED-Node, 0.97 ± 0.04 on PH2, 0.98 ± 0.02 on HAM-10000, and 0.97 ± 0.00 on ISIC-2019 datasets.
Keywords: skin cancer; skin segmentation; skin classification; melanoma; DeepLabv3+; CNN skin cancer; skin segmentation; skin classification; melanoma; DeepLabv3+; CNN

Share and Cite

MDPI and ACS Style

Zafar, M.; Amin, J.; Sharif, M.; Anjum, M.A.; Mallah, G.A.; Kadry, S. DeepLabv3+-Based Segmentation and Best Features Selection Using Slime Mould Algorithm for Multi-Class Skin Lesion Classification. Mathematics 2023, 11, 364. https://doi.org/10.3390/math11020364

AMA Style

Zafar M, Amin J, Sharif M, Anjum MA, Mallah GA, Kadry S. DeepLabv3+-Based Segmentation and Best Features Selection Using Slime Mould Algorithm for Multi-Class Skin Lesion Classification. Mathematics. 2023; 11(2):364. https://doi.org/10.3390/math11020364

Chicago/Turabian Style

Zafar, Mehwish, Javeria Amin, Muhammad Sharif, Muhammad Almas Anjum, Ghulam Ali Mallah, and Seifedine Kadry. 2023. "DeepLabv3+-Based Segmentation and Best Features Selection Using Slime Mould Algorithm for Multi-Class Skin Lesion Classification" Mathematics 11, no. 2: 364. https://doi.org/10.3390/math11020364

APA Style

Zafar, M., Amin, J., Sharif, M., Anjum, M. A., Mallah, G. A., & Kadry, S. (2023). DeepLabv3+-Based Segmentation and Best Features Selection Using Slime Mould Algorithm for Multi-Class Skin Lesion Classification. Mathematics, 11(2), 364. https://doi.org/10.3390/math11020364

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop