Comparative Analysis of Cataract Eye Disease Detection Using Yolov8 and Yolov10 |
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| © 2024 by IJCTT Journal | ||
| Volume-72 Issue-10 |
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| Year of Publication : 2024 | ||
| Authors : Arkesha Shah | ||
| DOI : 10.14445/22312803/IJCTT-V72I10P121 | ||
How to Cite?
Arkesha Shah , "Comparative Analysis of Cataract Eye Disease Detection Using Yolov8 and Yolov10," International Journal of Computer Trends and Technology, vol. 72, no. 10, pp. 141-147, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I10P121
Abstract
Cataracts represent a major global health challenge, which affects millions of individuals and causes vision impairment. Which generally happens with older people. Early detection and timely diagnosis are important to mitigate the impact of cataracts and prevent early eye vision loss. Automatic detection of cataracts in the Eyes can greatly assist healthcare professionals in early diagnosis and management and the prevention of blindness, ultimately improving patients' health outcomes and reducing the burden on healthcare resources. This study proposes a cataract detection system using YOLOv8, a cutting edge object detection model. By adapting YOLOv8 to the specific challenges of cataract detection, the aim is to develop a robust and efficient solution for automated cataract screening. The methodology involves training the YOLOv8 model on a comprehensive dataset of retinal images annotated with cataract labels. To assess the effectiveness of the proposed system, I evaluated its performance on a separate test set of retinal images, measuring key metrics such as precision, recall, and F1-score. The Comparative Performance Analysis of the YOLOV8 Model and YOLOV10 Model is also done. This evaluation aims to validate the system's ability to detect cataracts and its potential utility in clinical practice accurately.
Keywords
Cataract Eye Disease, YOLOV8, YOLOV10, Deep Learning Model,Object detection.
Reference
[1] Hind Hadi Ali, Ali Yakoob Al-Sultan, and Enas Hamood Al-Saadi, “Cataract Disease Detection Used Deep Convolution Neural Network,” 2022 5th International Conference on Engineering Technology and its Applications, Al-Najaf, Iraq, pp. 102-108, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Raghavendra Chaudhary, and Arun Kumar, “Cataract Detection Using Deep Learning Model on Digital Camera Images,” 2022 IEEE International Conference on Cybernetics and Computational Intelligence, Malang, Indonesia, pp. 489-493, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Shuvam Chakraborty, and Susovan Jana, “Early Prediction of Cataract using Convolutional Neural Network,” 2023 IEEE Devices for Integrated Circuit, Kalyani, India, pp. 446-450, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Md. Sajjad Mahmud Khan et al., “Cataract Detection Using Convolutional Neural Network with VGG-19 Model,” 2021 IEEE World AI IoT Congress, Seattle, WA, USA, pp. 209-212, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Zaidatul Nisa'Binti Abdul Basit, and Yoshihiro Mitani, “Cataract Disease Detection Based on Small Fundus Images Dataset Using CNNs,” 2024 2nd International Conference on Computer Graphics and Image Processing, Kyoto, Japan, pp. 113-117, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Kanwarpartap Singh Gill, Vatsala Anand, and Rupesh Gupta, “Cataract Detection Using optimized VGG19 Model by Transfer Learning perspective and its Social Benefits,” 2023 Second International Conference on Augmented Intelligence and Sustainable Systems, Trichy, India, pp. 593-596, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Ahsan Abbas et al., “A Transfer Learning Based Detection and Grading of Cataract using Fundus Images,” 2023 25th International Multitopic Conference, Lahore, Pakistan, pp. 1-6, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Deepak Kumar et al., “Cataract Disease Identification Using Transformer and Convolution Neural Network: A Novel Framework,” 2023 3rd International Conference on Technological Advancements in Computational Sciences, Tashkent, Uzbekistan, pp. 1230-1235, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Wajeeha Ahmed et al., “Automatic Diagnosis of Cataract and Myopia through Fundus Images,” 2023 International Conference on Business Analytics for Technology and Security, Dubai, United Arab Emirates, pp. 1-7, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Masum Shah Junayed et al., “CataractNet: An Automated Cataract Detection System Using Deep Learning for Fundus Images,” IEEE Access, vol. 9, pp. 128799-128808, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Syeda Nabila Shirazi et al., “Computer-Aided Diagnosis of Cataract Disease through Retinal Images,” 2022 International Conference on IT and Industrial Technologies, Chiniot, Pakistan, pp. 1-7, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[12] D. Shamia, Shajin Prince, and D. Bini, “An Online Platform for Early Eye Disease Detection using Deep Convolutional Neural Networks,” 2022 6th International Conference on Devices, Circuits and Systems, Coimbatore, India, pp. 388-392, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Natalia Sokolova et al., “Pixel-Based Iris and Pupil Segmentation in Cataract Surgery Videos Using Mask R-CNN,” 2020 IEEE 17th International Symposium on Biomedical Imaging Workshops, Iowa City, IA, USA, pp. 1-4, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Ahmad Bondan Triyadi, Alhadi Bustamam, and Prasnurzaki Anki, “Deep Learning in Image Classification Using VGG-19 and Residual Networks for Cataract Detection,” 2022 2nd International Conference on Information Technology and Education, Malang, Indonesia, pp. 293-297, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Renato R. Maaliw et al., “Cataract Detection and Grading Using Ensemble Neural Networks and Transfer Learning,” 2022 IEEE 13th Annual Information Technology, Electronics and Mobile Communication Conference, Vancouver, BC, Canada, pp. 74-81, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[16] P. Nageswari et al., “Automatic Detection and Classification of Diabetic Retinopathy Using Modified UNET,” 2023 Third International Conference on Artificial Intelligence and Smart Energy, Coimbatore, India, pp. 1468-1471, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Riya Sharma et al., “Modified EfficientNetB3 Deep Learning Model to Classify Colour Fundus Images of Eye Diseases,” 2023 IEEE 5th International Conference on Cybernetics, Cognition and Machine Learning Applications, Hamburg, Germany, pp. 632-638, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Urmila Pilania et al., “An Optimized Hybrid Approach to Detect Cataract,” 2022 IEEE Global Conference on Computing, Power and Communication Technologies, New Delhi, India, pp. 1-5, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Pankaj Kumar, Sudhir Bhandari, and Vishal Dutt, “Pre-Trained Deep Learning-Based Approaches for Eye Disease Detection,” 2023 International Conference on Circuit Power and Computing Technologies, Kollam, India, pp. 1286-1290, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[20] K.M. Mayalekshmi, Abhishek Ranjan, and Rajendra Machavaram, “In-field Chilli Crop Disease Detection Using YOLOv5 Deep Learning Technique,” 2023 IEEE 8th International Conference for Convergence in Technology, Lonavla, India, pp. 1-6, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Hima Bindu Koncha et al., “Early Detection and Prediction of Cataract Using Deep Learning,” 2023 IEEE 8th International Conference for Convergence in Technology, Lonavla, India, pp. 1-6, 2023.
[CrossRef] [Google Scholar] [Publisher Link]