Real Time Glaucoma Prediction Using Y-UNet Classifier via Hardware Software Co-Design SMART System

S Sathiya Priya; J G R Sathiaseelan

doi:10.17485/IJST/v15i3.2174

Article

Real Time Glaucoma Prediction Using Y-UNet Classifier via Hardware Software Co-Design SMART System

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v15i3.2174

Year: 2022, Volume: 15, Issue: 3, Pages: 115-126

Original Article

Real Time Glaucoma Prediction Using Y-UNet Classifier via Hardware Software Co-Design SMART System

S Sathiya Priya^1*, J G R Sathiaseelan²

¹Department of Computer Science, Bishop Heber College, Bharathidasan University, Trichy, Tamil Nadu
²Associate Professor and Head, Department of Computer Science, Bishop Heber College, Bharathidasan University, Trichy, Tamil Nadu

*Corresponding Author
Email: [email protected]

Received Date:23 November 2021, Accepted Date:10 January 2022, Published Date:31 January 2022

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: To present a real-time algorithm that combines Yolov5 and UNetbased CNN predictions to classify small-sized images, particularly medical images. Methods: The proposed model combines the various phases of preprocessing, object detection, segmentation and classification through Kalman filter, Yolov5, U-net based on CNN. The model is derived from the three different datasets to create a novel classification algorithm for medical data. The dataset contains images of 136 glaucoma patients and 187 healthy images. Findings: The proposed Y-UNet classifier framework is used to classify glaucoma images based on their IOP and CCT from real-time dataset collection. The proposed framework accuracy of 98.75% is achieved in the run time performance at 0.18 seconds per image. The accuracy is higher by 1.66% and runtime performance reduced by 0.03 seconds when compared with standard classification methods. Additionally, threshold optimizer has minimized the overall losses and provides the most accurate result. Novelty: The Y-UNet classifier model proposed here enables the real-time glaucoma prediction tasks to be carried out through the use of a hardware-based system known as the Sensitive Mirror Analyzer and Retina Tracker. To the best of our knowledge, for the first time, the proposed algorithm has been implemented to improve the accuracy and run time performance.

Keywords: UNet; Yolov5; YUNet classifier; Sensitive Mirror Analyzer and Retina Tracker (SMART) system; Glaucoma disease

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Copyright

© 2022 Priya & Sathiaseelan. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Published By Indian Society for Education and Environment (iSee)