Abstract:
Glaucoma is the second most common cause of vision loss around the world, after
cataracts. However, unlike cataracts, glaucoma usually can't be fixed. The number of
people with glaucoma is expected to rise from 64 million in 2015 to 76 million in 2020 and
111 million in 2040. Finding early signs of glaucoma is still hard for eye doctors in
developing countries. In real life, there is always a gap between early diagnosis of a disease
and a patient's medical history. This makes it hard to classify people correctly. Most of the
time, there are four main problems with deep learning algorithms for detecting glaucoma.
These are a limited dataset, significant coefficients of the statistical data, measuring
intraocular pressure, and CDR. In recent studies, researchers have tried to improve the
accuracy of classification by using different combinations of features, statistical data, or
changes to deep learning algorithms. Using these optimal feature selection algorithms
raises the cost of computation. The goal of this study was to find glaucoma early and
improve the accuracy of classification. This was done with the help of Fundus Imaging and
a three-headed fusion of Convolutional Autoencoding Classifier Framework, Adaptive
Neuro Fuzzy logic, and statistical analysis using wavelet transformation. These techniques
help ophthalmologists diagnose and watch for eye diseases by giving them clear and
accurate information about the structure of the optic nerve head. The proposed model
network is optimized so that the image reconstruction error, the classification error based
on a task learning procedure, and the fuzzy logic glaucoma predictor based on the statistical
data model are all as small as possible. This article also gives a review and analysis of the
work that came before. Its accuracy is shown by getting an accuracy of 95.89% on the Kin
Hospital early glaucoma dataset. This shows that the architecture is new and accurate.
Based on the proposed clinical inputs, the ANFIS results come out to be 95.4%.
