K. Ramana
A. Venkata Ramana
A. Krishna Mohan
- Assistant Professor, Department of Computer Science and Engineering, Rajiv Gandhi University of Knowledge Technologies, Srikakulam, Andhra Pradesh, India
- Professor, Department of Computer Science and Engineering, GMR Institute of Technology, Rajam, Andhra Pradesh, India
- Professor, Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Kakinada, Andhra Pradesh, India
Abstract
Cataracts, 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, random forest, logistic regression, and K-nearest neighbors 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.
Keywords: Cataract, machine learning, classification, support vector machine (SVM), K-nearest neighbors (KNN), random forest
[This article belongs to International Journal of Computer Science Languages(ijcsl)]
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References
Tasin T, Habib MA. Computer-aided cataract detection using random forest classifier. In: Arefin MS, Kaiser MS, Bandyopadhyay A, Ahad MAR, Ray K, editors. Proceedings of the International Conference on Big Data, IoT, and Machine Learning: BIM 2021. Singapore: Springer Singapore; 2022. pp. 27–38.
Zhang XQ, Hu Y, Xiao ZJ, Fang JS, Higashita R, Liu J. Machine learning for cataract classification/grading on ophthalmic imaging modalities: a survey. Mach Intell Res. 2022; 19 (3): 184–208.
Masruroh SU, Rahman DA, Putri RA. Systematic literature review: detecting cataract with deep learning. In: 2022 10th International Conference on Cyber and IT Service Management (CITSM), Yogyakarta, Indonesia, September 20–21, 2022. pp. 01–05.
Mulati TS, Utaminingrum F. Hidden neuron analysis for detection cataract disease based on gray level co-occurrence matrix and back propagation neural network. In: 2021 International Conference on ICT for Smart Society (ICISS), Bandung, Indonesia, August 2–4, 2021. pp. 1–5.
Ramanathan G, Chakrabarti D, Patil A, Rishipathak S, Kharche S. Eye disease detection using machine learning. In: 2021 2nd Global Conference for Advancement in Technology (GCAT), Bangalore, India, October 1–3, 2021. pp. 1–5.
Zhang L, Li J, Han H, Liu B, Yang J, Wang Q. Automatic cataract detection and grading using deep convolutional neural network. In: 2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC), Calabria, Italy, May 16–18, 2017. pp. 60–65.
Bhavsar H, Ganatra A. EuDiC SVM: a novel support vector machine classification algorithm. Intell Data Anal. 2016; 20 (6): 1285–1305.
Mailagaha Kumbure M, Luukka P. A generalized fuzzy k-nearest neighbor regression model based on Minkowski distance. Granular Comput. 2022; 7 (3): 657–671.
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;14 (3): 140.
Junayed MS, Islam MB, Sadeghzadeh A, Rahman S. CataractNet: an automated cataract detection system using deep learning for fundus images. IEEE Access. 2021; 9: 128799–128808.
| Volume | 01 |
| Issue | 02 |
| Received | August 28, 2023 |
| Accepted | September 22, 2023 |
| Published | November 24, 2023 |