CNN Architecture for Fundus Image Denoising, Robust Feature Extraction and Classification of Features by Using an Ensemble Classifier in Diabetic Retinopathy
Keywords:
Diabetic Retinopathy, Denoising, filering, classification, computer vision.Abstract
Diabetic retinopathy is considered as the leading cause of vision loss globally. Specifically, it is a microvascular disease that affects the retina resulting in vessel blockage which leads to deprive the retina tissues from nutrition. Effective treatment is possible if it is detected in its early stage because severe and proliferate stages can cause permanent blindness or vision loss.However, this process of DR identification depends on the skills of ophthalmologists, which are sometimes costly and time-consuming. Automatic detection systems were thus developed in an effort to speed up and reduce the cost of the identification process, making it accessible worldwide. The accuracy of the produced predictions, however, was somewhat unsatisfactory for eye doctors to depend on them as diagnostic systems due to the limited credible datasets and medical records for this specific eye illness. Moreover, the captured data suffer from various types of noise. Thus, removing the noise also becomes an important task of this research. As a result, we investigated on a combined denoising and ensemble-based learning technique for filtering and classification. The filtering is done by using CNN based architecture which uses residual noise mapping to identify the noise. In the next stage, we present CNN based feature extraction model and classify the obtained features by using an ensemble classifier. The experimental study shows that the proposed approach achieves classification performance of 97.89, 98.1, and 98.20% in terms of Accuracy, Precision, and Recall for the Kaggle dataset.
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