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K. Ramana, A. Venkata Ramana, A. Krishna Mohan
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- Assistant Professor, Professor, Professor, Department of Computer Science and Engineering, Rajiv Gandhi University of Knowledge Technologies, Srikakulam, Department of Computer Science and Engineering, GMR Institute of Technology, Rajam, Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Kakinada, Andhra Pradesh, Andhra Pradesh, Andhra Pradesh, India, India, India
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Abstract
nCataracts, characterized by dense cloudy areas in the eye’s lens, afflict more than 50% of elderly individuals, leading to impaired vision and potential blindness. Detecting cataracts at an early stage is crucial to facilitate simpler treatments, as neglecting the condition may necessitate complex eye surgery. To address this issue, we are creating a predictive system that identifies cataract disease by analyzing user-provided eye features. To achieve this, we leverage OpenCV, a Python library offering a range of image processing and feature extraction capabilities. Our approach incorporates Machine Learning Classification techniques such as Support Vector Machines (SVM), Random Forest, Logistic Regression, and K-Nearest Neighbors (KNN) to construct a robust predictive model. The overarching objective of this project is to develop a dependable and precise tool for the early detection and prediction of cataract disease. This tool holds the potential to enhance patient outcomes and reduce healthcare expenses significantly.
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Keywords: Cataract, machine learning, classification, SVM, KNN, random forest
n[if 424 equals=”Regular Issue”][This article belongs to International Journal of Computer Science Languages(ijcsl)]
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References
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1. Tasin T, Habib MA. Computer-Aided Cataract Detection Using Random Forest Classifier. InProceedings of the International Conference on Big Data, IoT, and Machine Learning: BIM 2021 2022 (pp. 27–38). Springer Singapore.
2. Zhang XQ, Hu Y, Xiao ZJ, Fang JS, Higashita R, Liu J. Machine learning for cataract classification/grading on ophthalmic imaging modalities: A survey. Machine Intelligence Research. 2022 Jun;19(3):184–208.
3. Masruroh SU, Rahman DA, Putri RA. Systematic Literature Review: Detecting Cataract With Deep Learning. In2022 10th International Conference on Cyber and IT Service Management (CITSM) 2022 Sep 20 (pp. 01–05). IEEE.
4. Mulati TS, Utaminingrum F. Hidden Neuron Analysis for Detection Cataract Disease Based on Gray Level Co-occurrence Matrix and Back Propagation Neural Network. In2021 International Conference on ICT for Smart Society (ICISS) 2021 Aug 2 (pp. 1–5). IEEE.
5. Ramanathan G, Chakrabarti D, Patil A, Rishipathak S, Kharche S. Eye disease detection using Machine Learning. In2021 2nd Global Conference for Advancement in Technology (GCAT) 2021 Oct 1 (pp. 1–5). IEEE.
6. Zhang L, Li J, Han H, Liu B, Yang J, Wang Q. Automatic cataract detection and grading using deep convolutional neural network. In2017 IEEE 14th international conference on networking, sensing and control (ICNSC) 2017 May 16 (pp. 60–65). IEEE.
7. Bhavsar H, Ganatra A. EuDiC SVM: A novel support vector machine classification algorithm. Intelligent Data Analysis. 2016 Jan 1;20(6):1285–305.
8. Mailagaha Kumbure M, Luukka P. A generalized fuzzy k-nearest neighbor regression model based on Minkowski distance. Granular Computing. 2022 Jul;7(3):657–71.
9. Murorunkwere BF, Ihirwe JF, Kayijuka I, Nzabanita J, Haughton D. Comparison of Tree-Based Machine Learning Algorithms to Predict Reporting Behavior of Electronic Billing Machines. Information. 2023 Feb 21;14(3):140.
10. Junayed MS, Islam MB, Sadeghzadeh A, Rahman S. CataractNet: An automated cataract detection system using deep learning for fundus images. IEEE Access. 2021 Sep 15;9:128799–808.
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| Volume | 01 |
| Issue | 02 |
| Received | August 28, 2023 |
| Accepted | September 22, 2023 |
| Published | October 21, 2023 |
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