Publication

Abstract : Glaucoma detection is a significant problem to be solved in medical field. Few research works have been designed to detect glaucoma in its early stage. But, performance of glaucoma disease detection using existing techniques was not effectual. Moreover, time complexity of conventional glaucoma disease detection was more. In order to overcome such limitations, Damped Least-Squares Recurrent Deep Neural Classification (DLRDNC) Technique is proposed. The DLRNL Technique designs DLS-Recurrent Deep Neural Classifier in order to increase the prediction performance of glaucoma disease at an early stage with minimal time. The DLRNL Technique conducts simulation process using metrics such as disease detection accuracy, disease detection time and false positive rate with respect to different number of fundus image. The simulation results depict that the DLRNL Technique is able to increase the accuracy and also reduces the amount of time required for glaucoma disease detection when compared to the state-of-the-art works.