Early Detection of The Glaucoma and Other Intra-Ocular Pressure Elevation Diseases Using Hardware Efficient Machine Learning Approach

Abstract

Nowadays, Glaucoma is one of the chronical diseases that entirely make the human eyes into the blindness. This disease is a consequence of an accumulation of aqueous humor in the eye due to a defect of its drainage system. This condition progressively elevates the intra-ocular pressure (IOP), affecting the optic nerve and resulting in permanent blindness if left untreated. In early stages, the glaucoma may be an asymptomatic. Hence, the proposed method is designed to detect the early stage of the glaucoma. This can be done by measuring the cup to disk ratio. For that, the proposed image processing algorithm is constrained with the three basic steps such as preprocessing, feature extraction and classification. In classification stage, we employ the SVM classifier to classify the normal and glaucoma images. The method is found to be efficient in hardware implementation when compared to other methods. The overall implementation will be held in the Matlab supporting environment.