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Olaniyi, Ebenezer Obaloluwa, Komolafe, Temitope Emmanuel, Oyedotun, Oyebade Kayode, Oyemakinde, Tolulope Tofunmi, Abdelaziz, Mohamed, Khashman, Adnan. (1401). Eye Melanoma Diagnosis System using Statistical Texture Feature Extraction and Soft Computing Techniques. سامانه مدیریت نشریات علمی, 13(1), 77-88. doi: 10.31661/jbpe.v0i0.2101-1268
Ebenezer Obaloluwa Olaniyi; Temitope Emmanuel Komolafe; Oyebade Kayode Oyedotun; Tolulope Tofunmi Oyemakinde; Mohamed Abdelaziz; Adnan Khashman. "Eye Melanoma Diagnosis System using Statistical Texture Feature Extraction and Soft Computing Techniques". سامانه مدیریت نشریات علمی, 13, 1, 1401, 77-88. doi: 10.31661/jbpe.v0i0.2101-1268
Olaniyi, Ebenezer Obaloluwa, Komolafe, Temitope Emmanuel, Oyedotun, Oyebade Kayode, Oyemakinde, Tolulope Tofunmi, Abdelaziz, Mohamed, Khashman, Adnan. (1401). 'Eye Melanoma Diagnosis System using Statistical Texture Feature Extraction and Soft Computing Techniques', سامانه مدیریت نشریات علمی, 13(1), pp. 77-88. doi: 10.31661/jbpe.v0i0.2101-1268
Olaniyi, Ebenezer Obaloluwa, Komolafe, Temitope Emmanuel, Oyedotun, Oyebade Kayode, Oyemakinde, Tolulope Tofunmi, Abdelaziz, Mohamed, Khashman, Adnan. Eye Melanoma Diagnosis System using Statistical Texture Feature Extraction and Soft Computing Techniques. سامانه مدیریت نشریات علمی, 1401; 13(1): 77-88. doi: 10.31661/jbpe.v0i0.2101-1268

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

Journal of Biomedical Physics and Engineering
مقاله 9، دوره 13، شماره 1، فروردین و اردیبهشت 2023، صفحه 77-88    اصل مقاله (1.53 M)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2101-1268
نویسندگان
Ebenezer Obaloluwa Olaniyi* 1، 2؛ Temitope Emmanuel Komolafe3؛ Oyebade Kayode Oyedotun4؛ Tolulope Tofunmi Oyemakinde5؛ Mohamed Abdelaziz6؛ Adnan Khashman7
1Center for Quantum Computational System, Department of Electrical and Electronics Engineering, Adeleke University, Osun State, Nigeria
2European Centre for Research and Academic Affairs, Lefkosa, Turkey
3Department of Medical Imaging, Suzhou Institute of Biomedical and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
4Interdisciplinary Centre for Security, Reliability, and Trust (SnT), University of Luxembourg, Luxembourg
5Department of Electrical and Electronics Engineering, Adeleke University, Ede, Nigeria
6Department of Biomedical Engineering, Shenzhen University, Shenzhen, China
7European Centre for Research and Academic Affairs, Turkey
چکیده
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.
کلیدواژه‌ها
Eye Melanoma؛ Neural Network؛ Radial Basis Function؛ Texture Analysis؛ Diagnostic Errors؛ Physicians؛ Models؛ Eye-Cancer؛ Neural Network, Computer
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