A robust framework for glaucoma detection using CLAHE and EfficientNet

Glaucoma disease is affecting a large community worldwide. It gradually affects the optic nerve and may cause partial or complete vision loss. Glaucoma happens due to an increase in the fluid pressure inside the optic nerve, which is also known as intraocular pressure (IOP). Therefore, it is essential to detect it in the early stage to prevent blindness. Recently, deep neural networks have been applied to analyse medical imagery. This paper proposes a framework for glaucoma detection using the deep convolution neural network. In this framework, a preprocessing step uses the CLAHE to enhance the local contrast. Further, we have utilized two segmentation models (EfficientNet + U-Net) for segmenting the optic cup and disc mask from retinal fundus images. Moreover, the CDR ratio is computed from the segmented optic cup and disc masks. The framework detects whether the inputted image is glaucoma infected or not based on the CDR ratio. The accuracy of the proposed framework is compared to various baseline models. A qualitative and quantitative assessment has been done on various benchmark datasets (DRISHTI-GS1 and RIM-ONE). The experimental outcomes illustrate that the proposed framework outperformed the other state-of-the-art methods for glaucoma detection in the retinal fundus image.