IMPROVED CNN BASED DATA ANALYTIC MODEL FOR DIABETIC RETINOPATHY PREDICTION
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 11, Pages 790-804
AbstractBig Data Analytic model examines large datasets and reveals the hidden information like hidden patterns and their correlations in it. Especially in the healthcare analytics, they are widely used for analyzing and predicting the diseases from the data that are being collected from various sources like electronic health record, patient’s insurance claim, scan reports, pharmaceutical, and research development data, emails, mobile devices, databases, applications, server and by other means. Diabetes is a chronic, incurable disease due to the abnormal levels of glucose in the blood. Diabetes is a major health problem that is increasing worldwide as it is a stern difficult complaint distressing the whole body. It needs regular care by ourselves. It would significantly impact the quality of life if complications are developed as it would also reduce life expectancy. Diabetic patients majorly suffer due to the problem of Diabetic Retinopathy which causes vision loss. To overcome the problem of Diabetic Retinopathy, diabetic patients should have a periodic ophthalmologic examination. Basically, detection of diabetic retinopathy (DR) through color fundus images and identifying the current status of the eye requires highly experienced physicians. The proposed Diabetic Retinopathy prediction model applies Modified Convolutional Neural Network(M-CNN). This model analyses DR from digital fundus images and correctly categorizing its severity. Also, the model will recognize the complex structures convoluted in the cataloging task such as micro-aneurysms, exudate, and bleedings on the retina and subsequently recognizes the problem without the need of manual intervention. The system is trained using publicly available Kaggle Dataset and the result is validated with the original fundus images of 100 patients.
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