Segmentation of Retinal Layers in OCT for Identification of Neural Disorders by DLL Models

Segmentation of Retinal Layers in OCT for Identification of Neural Disorders by DLL Models

Varshitha S1, Yashaswini H U2, Kshema R Gowda3, Impana S M4, Madhu C K

1 Department  of CSE, Malnad College of Engineering, Hassan

2Department of CSE, Malnad College of Engineering, Hassan

3 Department of CSE, Malnad College of Engineering, Hassan

4Department of CSE, Malnad College of Engineering, Hassasn

5Assistant Professor, Department of CSE, Malnad College of Engineering, Hassan

Abstract - Segmentation of the retinal layer in OCT images is very vital for early detection of neurological disorders such as glaucoma, Alzheimer's disease, and multiple sclerosis. Early detection of these diseases can bring tremendous difference to the patient—it enables physicians to initiate

treatment earlier and results in improved outcomes and increased quality of life for the patient.But today, much of this analysis is still performed manually. Physicians must review the images and interpret them by hand, a time consuming process that can produce varying results based on the person conducting the analysis. That sort of subjectivity and variability make it more difficult to make certain, timely diagnoses.To assist in addressing this issue, our study presents an automated, machine learning–enabled system that is capable of segmenting the retina layers in OCT images without the necessity of continuous human intervention.