AIML and DL Based CSR Disease Detection for OCT and Fundus Imaging

Millions of people Throughout the world suffer from Central Serous Retinopathy (CSR). CSR is a retinal disease that results in blindness and visual loss. The accumulation of watery fluid behind the retina causes the CSR.Detection and prevention of CSR disease helps to take preventive measures to avoid and overcome any damages to the human eye. there are several cutting-edge techniques and studies that support the automated identification of CSR. Presently AIML based methods are giving promising results. This work implements AIML and DL based innovative technology that contribute to the automated CSR detection. For the purpose of detecting CSR disease and analyzing the results 2 imaging approaches and 3 classification algorithms were used. Optical Coherence Tomography Angiography (OCT), Fundus Imaging are the two imaging or scanning (dataset) techniques used in this work and AIML and DL based techniques such as Convolutional Neural Network (CNN), Support Vector Machine (SVM), Random Forest (RF) are classification algorithms those are used for detection of CSR disease. Finally, our study shows through experimentation that current Deep Learning (DL) classifiers produces accurate, quick, and reliable CSR disease detection results, that is CNN performs better as compared other two techniques.