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Detect-DR: Classification of Diabetic Retinopathy using Fractal Analysis and Random Forest

Poonam R. Yadav, Revati V. Yadav, Sneha S. Pawar, Kajal S. Malusare, S. B. Shirke

Abstract


In this project we are implementing a Diabetic Retinopathy Detection System, which will be used primarily in medical institutes to detect and classify various stages of Diabetic Ratinopathy. This project will have primarily three main Users that would be Admin, Doctor and Patient. The complete system revolves around two users – Doctor and Patient. We are using dataset from Kaggle to train the system using Python and for main users like admin, doctor and patient UI would be web-based system designed and developed using HTML, CSS, JS, along with HOPE UI framework and Bootstrap 5. There will be a Web Application for DDR. Main Goal of the project is to automate the screening process of detection of diabetic ratinopathy. Project has 5 main modules, 1. Training, 2. Testing, 3. Admin Portal, 4. Doctor Portal, 5. Patient Portal through which all the task will be performed. Our system classifies Diabetic Retinopathy in 4 different.


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References


Kamble, V. V., & Kokate, R. D. (2020). Automated diabetic retinopathy detection using radial basis function. Procedia Computer Science, 167, 799-808.

Alzami, F., Megantara, R. A., & Fanani, A. Z. (2019, September). Diabetic retinopathy grade classification based on fractal analysis and random forest. In 2019 International Seminar on Application for Technology of Information and Communication (iSemantic) (pp. 272-276). IEEE.

Omar, Z. A., Hanafi, M., Mashohor, S., Mahfudz, N. F. M., & Muna'im, M. (2017, October). Automatic diabetic retinopathy detection and classification system. In 2017 7th IEEE International Conference on System Engineering and Technology (ICSET) (pp. 162-166). IEEE.


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