Recent Trends in Early Detection of Ocular Disorders Using Image Processing, Signal Processing, Quantum Models and Related Approaches

Ocular disorders like refractive errors, cataract, diabetic retinopathy, glaucoma and age-related macular degeneration are the major causes of blindness worldwide. Due to expensive medical equipments/procedures and disproportionate doctor to patient ratio, early diagnosis of ocular disorders is cumbersome; motivating researchers to automate the early detection process. In this review paper, the authors have briefed about some noteworthy attempts towards early detection of ocular disorder. This paper contains review on: focal biologically inspired feature(BIF) for Glaucoma Detection, three image processing techniques for glaucoma detection, BIF for peripapillary atrophy(PPA) detection, automatic method to find thickness of retinal nerve fibre layer(RFNL) from OCT images, computer aided retinal fundus image analysis scheme, discrete wavelet based glaucoma diagnosis, novel image processing method for glaucoma detection, algorithm based on Cup to Disc Ratio for glaucoma detection, Fuzzy expert system for glaucoma detection, computational tool for glaucoma detection, glaucoma detection approach using 1-D images, CNN based glaucoma detection framework, wavelet scattering based approach on fundus images, ensemble based deep learning method for identification and detection of glaucoma, comparative analysis of eight ImageNet models, cloud based framework for glaucoma screening, glaucoma detection using optimized algorithms like EPO & BFO, ImageNet based neural network classification model, intraocular pressure(IOP) based glaucoma detection and two quantum machine learning based approaches.