Accurate Segmentation and Classification of Glaucoma Disease Utilising Grey Wolf Based U-Net++ with Capsule Network
Keywords:
Capsule Network (CapsNet), Contrast Limited Adaptive Histogram Equalization (CLAHE), Histogram Equalization (HE), U-Shape Network technique (UNet )Abstract
Glaucoma, an eye condition marked by elevated eye pressure, is one of the leading causes of complete blindness for those affected. Early intervention through treatment and screening programs for glaucoma may reduce its detrimental consequences and help keep people seeing clearly. Glaucoma testing can be complex, and finding qualified healthcare providers to conduct assessments may prove challenging. Delays in diagnosis and treatment lead to an increasing global population suffering blindness or vision impairment. An automated screening system must be created quickly to address the limitations of existing manual screening methods, enabling early detection and treatment of abnormalities within either the Optic Cup (OC), Optic Disc or both eye regions. Accurate classification has become even more challenging as abnormalities overlap visually with the natural colour of eye tissue. This research paper introduces an automated framework designed to detect glaucoma. Our approach involves two stages - segmentation and classification - both crucial for an accurate diagnosis. At UNet++ we have developed a novel initial segmentation architecture combining the Grey Wolf optimization algorithm and UNet++ architecture specifically tailored for extracting optic disk from retinal fundus images. This approach uses an automatic evolutionary model which intelligently determines optimal and precise network configuration parameters through the Grey Wolf Optimization Algorithm (GWOA). Once the segmentation is complete, for the accurate classification of glaucoma, we utilize CapsNet (an advanced deep learning architecture known for its effectiveness in image recognition tasks) for this task. Our proposed method achieves an outstanding accuracy rate of 98.23% - surpassing other approaches and highlighting its power to enhance the accuracy and efficiency in diagnosing glaucoma. This remarkable feat highlights our automated system's potential to improve accuracy and efficiency when diagnosing this condition.
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