Retinal Based Pathology Analysis Using Deep Learning Approaches

Abstract

: Deep learning is an excellent approach to biomedical image segmentation tasks that help to identify and quantify patterns. Segmentation of biomedical images typically involves partitioning an image into multiple regions representing anatomical objects of interest. Biomedical image processing is a very broad field. Here, input is given as an image to extract features from an image to detect diseases. Unsupervised learning technique is used because it enables the model to uncover trends and facts that were previously undetected on its own. Human eye retina can provide valuable information about human health. The condition of the retinal vessels has been found to be a good indicator of overall health. For the prediction of retinal disease, it is classified as a retinal background, retinal vessels and optic disc. The disease can be predicted by this classification using the Singular Spectrum Analysis, Deep Neural Network, KNN Classifier and Eclipse Fitting algorithm. Singular Spectrum analysis was carried out to predict cross-section profiles for the detection of micro aneurysms. Diabetic diseases are classified under the KNN classification. Deep Neural Network algorithm is used for predicting cardiovascular disease and other disease such as glaucoma, stroke. Using the Eclipse Fitting algorithm, the optical disc and cup pixels are segmented to check whether or not a person's health is normal.