Open AccessJIEB-linknet
Author(s) -
S. Shajun Nisha,
M. Nagoor Meeral,
M. Mohamed Sathik
Publication year - 2022
Publication title -
international journal of health sciences (ijhs) (en línea)
Language(s) - English
Resource type - Journals
eISSN - 2550-6978
pISSN - 2550-696X
DOI - 10.53730/ijhs.v6ns1.5009
Subject(s) - segmentation , optical coherence tomography , computer science , artificial intelligence , retinal , glaucoma , encoder , deep learning , macular degeneration , identification (biology) , optometry , diabetic retinopathy , pattern recognition (psychology) , ophthalmology , medicine , biology , botany , diabetes mellitus , endocrinology , operating system
Age-related Macular Degeneration, Diabetic Retinopathy, Edema as well as Glaucoma are considered as leading ophthalmology diseases result in permanent blindness unless diagnosed earlier. Vision Impairment may be associated with factors such as hereditary, lifestyle and age. Structural changes and fluid formation in the retina helps to investigate the prognosis. Optical coherence Tomography helps to visualize the retinal microstructures in a non-invasive manner. Identification of disease progression is time demanding when these OCT biomarkers are segmented by human experts. Recent findings show that, Semantic segmentation in deep learning is effective for class imbalance problems. This research work proposes an improved LinkNet architecture to delineate retinal layers and fluids concerning OCT images. The primary intention concerning the proposed segmentation network is to categorize 7 retinal layers and fluid as distinct classes. Two Joint input encoder blocks are included in the architecture, which accept multiple inputs from prior layers in order to preserve high resolution characteristics for exact segmentation. In addition, the hybrid loss functions were utilized for improving the prediction accuracy. This model was assessed over the public Duke dataset and the outputs demonstrate that this model attains a dice coefficient of 0.9.