Enhancing the classification algorithms for the detection of central serous chorioretinopathy from optical coherence tomography images

Abstract

Computer-aided diagnosis and medical imaging tools have been developed to address numerous problems in daily life. This technological advancement enhances the efficacy and accuracy of clinical care and sickness diagnosis. Many researchers underwent the detection of Central Serous Chorioretinopathy (CSCR) disease. An efficient detection technique is thus needed to accurately assess the situation and get ready for systemic therapy to preserve or restore vision in the afflicted eye. Highly valued and continuous ophthalmology research has made it possible to identify a variety of retinal diseases, which helps the ophthalmologist plan and administer prompt treatment. In ophthalmology, there are numerous imaging tools with different levels of resolution that are used for retinal inspection. Such imaging systems produce images with noise, which must be removed to produce correct results. Regular screening is one of the more challenging tasks due to the greater number of patients with retinal defects and the smaller number of medical professionals. In a screening environment, it enables a faster, more objective analysis of a large number of images than traditional observerdriven systems. It can be a valuable diagnostic tool in a clinical context by lessening the burden of professional graders and associated expenses. newline