Abstract
Diabetic retinopathy (DR) is a widespread difficulty of diabetes and is considered as a main reason for vision loss in all over the globe. Several difficulties of DR can be avoided by controlling blood glucose level and timely medication. In real time, it is difficult to detect the DR and consumes more time in a manual way. This paper introduces a new Gradient Descent (GD) based Hyper parameter tuned Xception model called GD-Xception model to detect and classify DR images in an effective way. The GD-Xception model involves a series of subprocesses namely preprocessing, segmentation, feature extraction and classification. A set of extensive simulation takes place to ensure the effective outcome of the presented GD-Xception model. The presented model is tested using a DR dataset from Kaggle. The extensive experimental study clearly portrayed the superior outcome of the GD-Xception model with the maximum accuracy, sensitivity and specificity of 99.39%, 98.50% and 99.62% respectively.
Highlights
Diabetes plays a crucial role in several humans; even it is caused for animals that lead to fatal if it has not been detected at primary stage
This paper introduces a new Gradient Descent (GD) based Hyper parameter tuned Xception model called GD-Xception model to detect and classify Diabetic retinopathy (DR) images in an effective way
The GD-Xception model involves a series of subprocesses namely preprocessing, segmentation, feature extraction and classification
Summary
Diabetes plays a crucial role in several humans; even it is caused for animals that lead to fatal if it has not been detected at primary stage. The number of patients has been increased and there are lack of sufficient medical facilities in few regions, several individuals affected with DR could not undergo proper treatment and tends to blindness, and some alternate aspects caused due to vision loss These constraints can be limited by diagnosing DR in earlier stage and provide a suitable treatment to degrade the growth of Revised Manuscript Received on February 05, 2020. A major limitation present in this model is that, it is time consuming and arduous to create a technique from zero whereas transfer learning as well as hyperparameter tuning is applied in this study Such kinds of architectures are introduced in [12]. The GD-Xception model is an effective approach for automatically segments and classifies the DR images for detection and classification
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