Eye Melanoma Diagnosis System using Statistical Texture Feature Extraction and Soft Computing Techniques

Ebenezer Obaloluwa Olaniyi; Temitope Emmanuel Komolafe; Oyebade Kayode Oyedotun; Tolulope Tofunmi Oyemakinde; Mohamed Abdelaziz; Adnan Khashman

doi:10.31661/jbpe.v0i0.2101-1268

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Eye Melanoma Diagnosis System using Statistical Texture Feature Extraction and Soft Computing Techniques

Authors

Olaniyi, Ebenezer Obaloluwa^{1, 2}
Komolafe, Temitope Emmanuel³
Oyedotun, Oyebade Kayode⁴
Oyemakinde, Tolulope Tofunmi⁵
Abdelaziz, Mohamed⁶
Khashman, Adnan⁷

¹ eria
² European Centre for Research and Academic Affairs, Lefkosa, Turkey
³ Department of Medical Imaging, Suzhou Institute of Biomedical and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
⁴ Interdisciplinary Centre for Security, Reliability, and Trust (SnT), University of Luxembourg, Luxembourg
⁵ Department of Electrical and Electronics Engineering, Adeleke University, Ede, Nigeria
⁶ Department of Biomedical Engineering, Shenzhen University, Shenzhen, China
⁷ European Centre for Research and Academic Affairs, Turkey

Type

Published Article

Journal

Journal of Biomedical Physics & Engineering

Publisher

Shiraz University of Medical Sciences

Publication Date

Feb 01, 2023

Volume

13

Issue

1

Pages

77–88

Identifiers

DOI: 10.31661/jbpe.v0i0.2101-1268

PMID: 36818006

PMCID: PMC9923246

Source

PubMed Central

Keywords

Disciplines

Original Article

License

Unknown

Abstract

Background: Eye melanoma is deforming in the eye, growing and developing in tissues inside the middle layer of an eyeball, resulting in dark spots in the iris section of the eye, changes in size, the shape of the pupil, and vision. Objective: The current study aims to diagnose eye melanoma using a gray level co-occurrence matrix (GLCM) for texture extraction and soft computing techniques, leading to the disease diagnosis faster, time-saving, and prevention of misdiagnosis resulting from the physician’s manual approach. Material and Methods: In this experimental study, two models are proposed for the diagnosis of eye melanoma, including backpropagation neural networks (BPNN) and radial basis functions network (RBFN). The images used for training and validating were obtained from the eye-cancer database. Results: Based on our experiments, our proposed models achieve 92.31% and 94.70% recognition rates for GLCM+BPNN and GLCM+RBFN, respectively. Conclusion: Based on the comparison of our models with the others, the models used in the current study outperform other proposed models.

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