Affiliations: [a] Department of Biomedical Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamil Nadu, India
| [b] Department of Computer Science, Arulmigu Subramania Swamy Arts and Science College, Vilathikulam, Tuticorin, Tamil Nadu, India
| [c] Department of ECE, SVS College of Engineering, Arasampalayam, Coimbatore, Tamil Nadu, India
| [d] School of Computing Science and Engineering, Galgotias University, Greater Noida, Uttarpradesh, India
Correspondence:
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Corresponding author. Malathi Murugesan, Department of Biomedical Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamil Nadu, India. E-mail: [email protected].
Abstract: Glaucoma is a condition of the eye that is caused by an increase in the eye’s intraocular pressure that, when it reaches its advanced stage, causes the patient to lose all of their vision. Thus, glaucoma screening-based treatment administered in a timely manner has the potential to prevent the patient from losing all of their vision. However, because glaucoma screening is a complicated process and there is a shortage of human resources, we frequently experience delays, which can lead to an increase in the proportion of people who have lost their eyesight worldwide. In order to overcome the limitations of current manual approaches, there is a critical need to create a reliable automated framework for early detection of Optic Disc (OD) and Optic Cup (OC) lesions. In addition, the classification process is made more difficult by the high degree of overlap between the lesion and eye colour. In this paper, we proposed an automatic detection of Glaucoma disease. In this proposed model is consisting of two major stages. First approach is segmentation and other method is classification. The initial phase uses a Stacked Attention based U-Net architecture to identify the optic disc in a retinal fundus image and then extract it. MobileNet-V2 is used for classification of and glaucoma and non-glaucoma images. Experiment results show that the proposed method outperforms other methods with an accuracy, sensitivity and specificity of 98.9%, 95.2% and 97.5% respectively.
Keywords: Medical image segmentation, classification, convolutional neural network, U-Net, MobileNet-V2
