Research Article
Detection of Diabetic Retinopathy Using CNN
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@INPROCEEDINGS{10.1007/978-3-030-99197-5_8, author={Raghad Abdulghani and Ghaida Albakri and Rawan Alraddadi and Liyakathunisa Syed}, title={Detection of Diabetic Retinopathy Using CNN}, proceedings={IoT Technologies for Health Care. 8th EAI International Conference, HealthyIoT 2021, Virtual Event, November 24-26, 2021, Proceedings}, proceedings_a={HEALTHYIOT}, year={2022}, month={3}, keywords={CNN Deep learning Diabetic retinopathy Median filter Morphology Interpolation}, doi={10.1007/978-3-030-99197-5_8} }- Raghad Abdulghani
Ghaida Albakri
Rawan Alraddadi
Liyakathunisa Syed
Year: 2022
Detection of Diabetic Retinopathy Using CNN
HEALTHYIOT
Springer
DOI: 10.1007/978-3-030-99197-5_8
Abstract
Diabetic retinopathy is one of the most common diseases for diabetic patients around the world. Moreover, this disease causes lesions on the retina which affect the vision of the patient. Hence, diabetic retinopathy may lead to blindness in some cases if not detected earlier. Therefore, early detection of this disease is required to prevent vision loss. In this paper, deep learning techniques were used to produce a good performance in detecting and classifying fundus images. The proposed method is an implementation of CNN algorithm that detects and classifies fundus images based on the stage of the disease. As a result, the accuracy we obtained in our approach has reached 92.26% and MSE of 0.0628.
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