/Diabetic_retinopathydetection

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Diabetic_retinopathydetection

This project is part of the classwork for The Machine learning Course (SBE3021), Faculty of Engineering, Cairo University.

Under supervision of Dr: Inas Yassine and Eng. Merna Bebars

Contributors

* Aya Sameh

* Ehab Kamal

* Hanya Ahmad

* Mohamed Hashem

Diabetic retinopathy is a common complication of diabetes and a leading cause of vision loss in adults. It occurs when high blood sugar levels damage the blood vessels in the retina, leading to vision impairment and even blindness if left untreated. Early detection is crucial for managing the progression of this disease.

In this project, we aim to develop a model that can accurately detect diabetic retinopathy using a dataset of fundus images.

the following images are generated from processing fundus images :-

  • vessels

  • microaneursyms

  • exudates

    GLCM features are extracted from the previous images type and fed into an svm vinary classifier achieving an accuracy of 88%.