Abstract:
The current challenge is early and automated diagnosis and prognosis of glaucoma using information technology based on machine and deep learning methods. The conducted analysis of the methods and tools for diagnosing and predicting the glaucoma has shown that now there are
many such methods and tools, including those based on machine learning and deep learning, but all of them have certain drawbacks, such as their "niche" (lack of mass use, development of tools exclusively for proving and testing the theoretical positions developed by the authors), complexity of development, complexity of use, high cost, the need for an ophthalmologist to decipher the data obtained, etc. Therefore, the aim of this study is to develop the information technology for diagnosis and prognosis of glaucoma based on machine learning methods, which will have minimal requirements and resource needs, be characterized by low cost and mass use, and will not require an ophthalmologist to decipher the data generated by the neural network. The proposed information technology for diagnosis and prognosis of glaucoma based on machine learning methods automates the processing of fundus retinal images and optical coherence tomography images based on machine learning in order to automatically diagnose glaucoma at early stages by classifying the eye as normal or glaucomatous.
