Olaniyi Ebenezer Obaloluwa, Komolafe Temitope Emmanuel, Oyedotun Oyebade Kayode, Oyemakinde Tolulope Tofunmi, Abdelaziz Mohamed, Khashman Adnan
Center for Quantum Computational System, Department of Electrical and Electronics Engineering, Adeleke University, Osun State, Nigeria.
European Centre for Research and Academic Affairs, Lefkosa, Turkey.
J Biomed Phys Eng. 2023 Feb 1;13(1):77-88. doi: 10.31661/jbpe.v0i0.2101-1268. eCollection 2023 Feb.
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.
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.
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.
Based on our experiments, our proposed models achieve 92.31% and 94.70% recognition rates for GLCM+BPNN and GLCM+RBFN, respectively.
Based on the comparison of our models with the others, the models used in the current study outperform other proposed models.
眼黑色素瘤在眼内会造成变形，在眼球中层的组织中生长和发展，导致眼部虹膜部分出现黑斑、瞳孔大小和形状改变以及视力变化。
本研究旨在使用灰度共生矩阵（GLCM）进行纹理提取和软计算技术来诊断眼黑色素瘤，从而实现更快的疾病诊断、节省时间并防止因医生手动方法导致的误诊。
在本实验研究中，提出了两种用于诊断眼黑色素瘤的模型，包括反向传播神经网络（BPNN）和径向基函数网络（RBFN）。用于训练和验证的图像来自眼癌数据库。
基于我们的实验，我们提出的模型对于GLCM + BPNN和GLCM + RBFN分别实现了92.31%和94.70%的识别率。
基于我们的模型与其他模型的比较，本研究中使用的模型优于其他提出的模型。
