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End date: 2022Subject: search.filters.subject.Diabetic Retinopathy

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    DIABETIC RETINOPATHY LESIONS IDENTIFICATION IN THE COLOR FUNDUS IMAGES USING MULTI-LAYER PERCEPTRON
    (IEEE, 2022-03-25) Geethalakshmi K; Meenakshi V.S
    The prognosis of Diabetic Retinopathy (DR) is characterized by Non-Proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR). The early stage of DR is known as NPDR. Detecting NPDR in the early stage becomes crucial to avoid blindness. The purpose of this study is to perceive NPDR lesions using image processing techniques and classification methods. The detection of lesions is carried out by pre-processing, feature extraction, feature vector construction, and classification. The vessel network is extracted for feature extraction in the pre-processing stage. Apart from the regular statistical image features, the color layer features are extracted from the smoothened input image. A clustering-based feature extraction method is introduced to capture features from each color layer. The filtered features, which produce the desired output, are combined and fed into Multi-Layer Perceptron (MLP) classifier. The proposed algorithm achieves 100% accuracy in detecting DR. Hence, this study shows that the proposed method can able to find the DR lesions in the early stage itself.
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    A SURVEY ON DEEP LEARNING APPROACHES IN RETINAL VESSEL SEGMENTATION FOR DISEASE IDENTIFICATION
    (Sankara College of Science and Commerce, 2018-10-10) K, Geethalakshmi
    Human retinal image plays a vital role in detection and diagnosis of various eye diseases for ophthalmologist. Automated blood vessel segmentation diagnoses many eye diseases like diabetic retinopathy, hypertension retinopathy, retinopathy of prematurity or glaucoma based on the feature extraction. Automated image analysis tool based on machine learning algorithms are the key point to improve the quality of image analysis. Deep learning (DL) is a subset of machine learning which is completely based on artificial neural network. It helps a machine to analyze the data efficiently. Deep learning is one extensively applied techniques that provides state of the art accuracy. Different types of neural network and platform used for DL also discussed. This paper reviews the different DL approaches for blood vessels segmentation. It concludes that the deep learning methods produces high level of accuracy in disease identification