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An IoT based predictive modeling for Glaucoma detection in optical coherence tomography images using hybrid genetic algorithm

  • 1211: AIoT Support and Applications with Multimedia
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Abstract

The primary cause of irreversible blindness due to glaucoma is a silent, progressive disease with no noticeable symptoms. This eye disease gradually and rapidly damages the optic nerve, resulting in visual field defects. Glaucoma can cause substantial vision loss if left untreated. Early detection and proper treatment help limit those severe consequences. The benefits of screening for glaucoma while reducing the workload on eye specialists outweigh the extra effort required for screening. With unmanned aircraft, self-driving cars, facial recognition, and language processing, artificial intelligence (AI) has altered our way of life. AI is capable of outperforming humans in tasks like image recognition. Data analysis and processing are critical, as the volume of image data generated by ophthalmic imaging centers continues to grow at a breakneck pace. Glaucoma has been predicted using OCT and fundus images of prospective patients and for this prediction; AI can be employed to help medical practitioners to come out of these problems. In this paper, a novel AI and Internet of Things (IoT) based predictive modeling is proposed in which a bio-inspired and artificial intelligence based computing approach is employed for classification and prediction of glaucoma disease from Optical coherence tomography (OCT) images through continuous monitoring. That ultimately results in an improvement in healthcare by providing necessary medical instructions. We are the frontrunners to present a unique IoT embedded with artificial intelligence that supports Glaucoma screening, an automated and timely system based on the fusion of machine learning and bio-inspired computing approaches, in the form of this study.150 OCT pictures were utilized in the experiment, which were derived from a mixture of MENDELEY and a private dataset by a renowned eye physicians. This work presents a solution to the question of how to diagnose this condition at an early stage utilizing 45 critical characteristics retrieved using the ORB feature extractor and custom algorithms. Our suggested model has four dimensions; originally, these 45 features were reduced to 20% (i.e., 9) utilizing a statistically based univariate selection procedure. Following that, a Genetic Algorithm (GA) is used to discover an optimum subset of characteristics. These optimized characteristics are routed sequentially to state-of-the-art machine learning models (K-Nearest Neighbor (KNN), XGBoost, Random Forest, and Support Vector Machine (SVM)) for classification. Additionally to the above, the technique is fully integrated into an IoT framework and can be accessed remotely to aid ophthalmologists in diagnosing and treating glaucoma. Additionally, the proposed model facilitates the collection of health data from patients through IoT devices. It is concluded that out of four possible sets of results, GA-KNN based combination input of 9 features, enhanced the computed results with 99% accuracy for glaucoma recognition. The accuracy is obtained through a fivefold cross-validation technique. Because the proposed system has a brilliant ability to differentiate between healthy and glaucomatous eyes, this study will help to achieve high standards of glaucoma identification.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Sharda University, Greater Noida, India

    Law Kumar Singh &  Pooja

  2. Department of Computer Science and Engineering, Hindustan College of Science and Technology, Mathura, India

    Law Kumar Singh & Munish Khanna

  3. Department of Computer Engineering and Applications, GLA University, Mathura, India

    Hitendra Garg

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Singh, L.K., Pooja, Garg, H. et al. An IoT based predictive modeling for Glaucoma detection in optical coherence tomography images using hybrid genetic algorithm. Multimed Tools Appl 81, 37203–37242 (2022). https://doi.org/10.1007/s11042-022-13540-5

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