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IECE Transactions on Intelligent Systematics, 2024, Volume 1, Issue 3: 145-160

Free to Read | Research Article | 29 October 2024
1 Institute of Management Sciences Peshawar, Peshawar, Pakistan
2 School of Electronic and Control Engineering, Chang’an University, Xián 710064, China
3 Department of Computer Science, Islamia University of Bahawalpur, Bahawalpur, Pakistan
4 School of Computer Science and Technology, Zhejiang Gongshang University, Hangzhou 310018, China
5 School of Mathematics and Statistics, Zhejiang Gongshang University, Hangzhou 310018, China
6 Department of Health Science and Technology, Gachon Advanced Institute for Health Sciences and Technology GAIHST, Gachon University, Incheon 21936, Korea
7 Department of Computer Science and Information Technology, University of Malakand, Chakdara, Pakistan
8 Department of Computer Science, Abdul Wali Khan University, Mardan 23200, Pakistan
* Corresponding Author: Tariq Hussain, [email protected]
Received: 29 September 2024, Accepted: 15 October 2024, Published: 29 October 2024  
Abstract
Cataracts are a leading cause of blindness in Pakistan, contributing to more than 54% of cases due to poor living condition, nutritional deficiencies, and limited healthcare access. Early detection is critical to avoid invasive treatments,but current diagnostic approaches often identify cataracts at advanced stages. This paper presents an advanced,automated cataract detection system using deep learning specifically the ResNet-50 architecture, to address this gap. The model processes fundus retinal images curated from diverse datasets, classified by ophthalmologic experts through a rigorous three-stage process. By leveraging the ResNet-50 model, cataracts are categorized into normal,moderate,and severe, achieving an accuracy of 97.56% on full images. Notably, the system performs well even on partial images with 70% visibility, maintaining an accuracy of 95.23%, thus minimizing the need for extensive images restoration. The dataset was augmented to include 17,500 images,ensuring robust training. The model's ability to detect cataracts with high precision in images with varying visibility(70% ,80%,85% and beyond) demonstrate its flexibility and reliability, consistently achieving accuracy above 95.50%. This research offers a non-invasive, efficient solution particularly suited for remote areas, addressing the limitations of the late-stage diagnoses. It represent a significant advancement in cataract detection and has the potential to revolutionize global cataracts identification through early, accurate intervention.

Graphical Abstract
Enhancing Ocular Health Precision: Cataract Detection Using Fundus Images and ResNet-50

Keywords
cataract detection
deep learning
ResNet-50
eye fundus images
health care
partial image

Funding
This work was supported without any funding.

Cite This Article
APA Style
Khan, I., Akbar, W., Soomro, A., Hussain, T., Khalil, I., Khan, M. N., & Salam, A. (2024). Enhancing Ocular Health Precision: Cataract Detection Using Fundus Images and ResNet-50. IECE Transactions on Intelligent Systematics, 1(3), 145-160. https://doi.org/10.62762/TIS.2024.640345

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