Development of Hybrid Wolf Optimization with Faster Mask RCNN to Predict the Healthcare System at an Earlier Stage
Keywords:
Diabetes Retinopathy; Early prediction; Faster Mask RCNN; Classifications; AccuracyAbstract
Diabetic Retinopathy (DR) is a retinal complication brought by the metabolic disorder diabetes, that is, the four primary causes of blindness worldwide. It can be difficult to diagnose DR because there are typically no obvious symptoms before the onset. If the need for DR identification is not effectively automated, the healthcare sector may suffer negative effects. As a result, we want to create a system for classifying DR samples that is both automated and economical. In this paper, we proposed the Faster Mask Recurrent Convolutional Neural Network (FM-RCNN) method for the identification of retinal images to classify DR lesions. We produce the dataset annotations needed for model training after pre-processing. The representative set of key points is then computed by introducing MD-ResNet at the FM-RCNN feature extraction level. After localizing and classifying the input sample into five categories, the FM-RCNN completes the process. With a 97.2% accuracy rate, the newly developed methodology excels, according to rigorous tests on a Kaggle dataset consisting of 88,704 visuals. We have contrasted our method with cutting-edge methods to demonstrate its robustness in terms of DR classification & localization. Additionally, we validated the Kaggle data across datasets and both the training and testing phases of the APTOS datasets and produced outstanding results.
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