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.
Keywords: Computer; Diagnostic Errors; Eye Melanoma; Eye-Cancer; Models; Neural Network; Physicians; Radial Basis Function; Texture Analysis.
Copyright: © Journal of Biomedical Physics and Engineering.