Effective Detection of Glaucoma from Fundus Images using Computational Intelligence Technique

Painless eye disease which manifests as gradual loss of vision is called as glaucoma. The accuracy of clinical techniques is less and early stage detection is not possible. The paper presents an automated detection method that uses wavelet features extracted from fundus image and classify them withartificial neural network. Proposed method detect glaucoma in very less time and accuracy is 92.8%.