Grey wolf optimization technique with U-shaped and capsule networks-A novel framework for glaucoma diagnosis

Abstract

The worldwide prevalence of glaucoma makes it a major reason for blindness thus proper early diagnosis remains essential for preventing major vision deterioration. Current glaucoma screening methods that need expert handling prove to be time-intensive and complicated before yielding appropriate diagnosis and treatment. Our system addresses these difficulties through an automated glaucoma screening platform which combines advanced segmentation methods with classification approaches. A hybrid segmentation method combines Grey Wolf Optimization Algorithm with U-Shaped Networks to obtain precise extraction of the optic disc regions in retinal fundus images. Through GWOA the network achieves optimal segmentation by adopting wolf-inspired behaviors such as circular and jumping movements to identify diverse image textures. The glaucoma classification depends on CapsNet as a deep learning model that provides exceptional image detection to ensure precise diagnosis. The combination of our method delivers 96.01 % segmentation together with classification precision which outstrips traditional approaches while indicating strong capabilities for discovering glaucoma at early stages. This automated diagnosis system elevates clinical accuracy levels through an automated screening method that solves manual process limitations. The detection framework produces better accuracy to improve clinical results in a strong effort to minimize glaucoma-induced blindness worldwide and display its capabilities in real clinical environments.•Hybrid GWOA-UNet++ for precise optic disc segmentation.•CapsNet-based classification for robust glaucoma detection.•Achieved 96.01 % accuracy, surpassing existing methods.

Keywords: Capsule network, Deep learning classification; Glaucoma detection; Grey Wolf Optimized UNet++ with Capsule Network for Automated Glaucoma Screening (GWO-UNet++-CapsNet).; Grey wolf optimization; Optic disc segmentation; U-Net++; Vision loss.

Graphical abstract

Fig. 1

Retinal Fundus Image.

Fig. 2

GWO with U-Net Architecture.

Fig. 3

GWO Correlation with the Detection Process.

Fig. 4

Pack allocation process.

Fig. 5

Variation differentiation for encircling process.

Fig. 6

Textural Difference Identification.

Fig. 7

U-Net Architecture for Proposed GWO Process.

Fig. 8

Caps-Network for reliable identification of Glaucomat or Normal.

Fig. 9

A Case Image from the ORIGA Fundus DataSet.

Fig. 10

Segmented output.

Fig. 11

Accuracy testing of the suggested Vs. (UNet++) + CapsNet.

Fig. 12

Accuracy Metrics for Training and Testing.

Fig. 13

Loss Metris for Training and Testing.

Fig. 14

Accuracy.

Fig. 15

Precision.

Fig. 16

Recall.

Fig. 17

Specificity.

Fig. 18

Sensitivity.