Effective Cataract Identification System using Deep Convolution Neural Network

Authors

  • P N Senthil Prakash Vellore Institute of Technology University image/svg+xml
  • S Sudharson Vellore Institute of Technology University image/svg+xml
  • Venkat Amith Woonna Vellore Institute of Technology University image/svg+xml
  • Sai Venkat Teja Bacham Vellore Institute of Technology University image/svg+xml

DOI:

https://doi.org/10.4108/eetpht.10.5525

Keywords:

CataractsNET, Cataract Detection, CNN, Deep Learning, Pre-trained networks

Abstract

INTRODUCTION: The paper introduces a novel approach for the early detection of cataracts using images captured using smartphones. Cataracts are a significant global eye disease that can lead to vision impairment in individuals aged 40 and above. In this article, we proposed a deep convolution neural network (CataractsNET) trained using an open dataset available in Github which includes images collected through google searches and images generated using standard augmentation mechanism.

OBJECTIVES: The main objective of this paper is to design and implement a lightweight network model for cataract identification that outperforms other state-of-the-art network models in terms of accuracy, precision, recall, and F1 Score.

METHODS: The proposed neural network model comprises nine layers, guaranteeing the extraction of significant details from the input images and achieving precise classification. The dataset primarily comprises cataract images sourced from a standardized dataset that is publicly available on GitHub, with 8000 training images and 1600 testing images.

RESULTS: The proposed CataractsNET model achieved an accuracy of 96.20%, precision of 96.1%, recall of 97.6%, and F1 score of 96.1%. These results demonstrate that the proposed method outperforms other deep learning models like ResNet50 and VGG19.

CONCLUSION: The paper concludes that identifying cataracts in the earlier stages is crucial for effective treatment and reducing the likelihood of experiencing blindness. The widespread use of smartphones makes this approach accessible to a broad audience, allowing individuals to check for cataracts and seek timely consultation with ophthalmologists for further diagnosis.

Downloads

Download data is not yet available.

References

Juyel Rana and Syed Md Galib. Cataract detection using smartphone. In 2017 3rd international conference on electrical information and communication technology (EICT), pages 1–4. IEEE, 2017. DOI: https://doi.org/10.1109/EICT.2017.8275136

Hitoshi Shichi. Cataract formation and prevention. Ex- pert opinion on investigational drugs, 13(6):691–701, 2004. DOI: https://doi.org/10.1517/13543784.13.6.691

Dennis Lam, Srinivas K Rao, Vineet Ratra, Yizhi Liu, Paul Mitchell, Jonathan King, Marie-Jose´ Tassignon, Jost Jonas, Chi P Pang, and David F Chang. Cataract. Nature reviews Disease primers, 1(1):1–15, 2015. DOI: https://doi.org/10.1038/nrdp.2015.14

David Allen and Abhay Vasavada. Cataract and surgery for cataract. Bmj, 333(7559):128–132, 2006. DOI: https://doi.org/10.1136/bmj.333.7559.128

Ji-Jiang Yang, Jianqiang Li, Ruifang Shen, Yang Zeng, Jian He, Jing Bi, Yong Li, Qinyan Zhang, Lihui Peng, and Qing Wang. Exploiting ensemble learning for auto- matic cataract detection and grading. Computer methods and programs in biomedicine, 124:45–57, 2016. DOI: https://doi.org/10.1016/j.cmpb.2015.10.007

Oluwatobi Joshua Afolabi, Gugulethu P Mabuza- Hocquet, Fulufhelo V Nelwamondo, and Babu Sena Paul. The use of u-net lite and extreme gradient boost (xgb) for glaucoma detection. IEEE Access, 9:47411–47424, 2021. DOI: https://doi.org/10.1109/ACCESS.2021.3068204

Lifeng Qiao, Ying Zhu, and Hui Zhou. Diabetic retinopathy detection using prognosis of microaneurysm and early diagnosis system for non-proliferative diabetic retinopathy based on deep learning algorithms. IEEE Access, 8:104292–104302, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.2993937

Seth R Flaxman, Rupert RA Bourne, Serge Resnikoff, Peter Ackland, Tasanee Braithwaite, Maria V Cicinelli, Aditi Das, Jost B Jonas, Jill Keeffe, John H Kempen, et al. Global causes of blindness and distance vision impairment 1990–2020: a systematic review and meta- analysis. The Lancet Global Health, 5(12):e1221–e1234, 2017.

Linglin Zhang, Jianqiang Li, He Han, Bo Liu, Jijiang Yang, Qing Wang, et al. Automatic cataract detection and grading using deep convolutional neural network. In 2017 IEEE 14th international conference on networking, sensing and control (ICNSC), pages 60–65. IEEE, 2017.

Turimerla Pratap and Priyanka Kokil. Computer-aided di- agnosis of cataract using deep transfer learning. Biomedical Signal Processing and Control, 53:101533, 2019. DOI: https://doi.org/10.1016/j.bspc.2019.04.010

Turimerla Pratap and Priyanka Kokil. Deep neural network based robust computer-aided cataract diagnosis system using fundus retinal images. Biomedical Signal Processing and Control, 70:102985, 2021. DOI: https://doi.org/10.1016/j.bspc.2021.102985

Shashwat Pathak and Basant Kumar. A robust automated cataract detection algorithm using diagnostic opinion based parameter thresholding for telemedicine applica- tion. Electronics, 5(3):57, 2016. DOI: https://doi.org/10.3390/electronics5030057

Xiaohang Wu, Lixue Liu, Lanqin Zhao, Chong Guo, Ruiyang Li, Ting Wang, Xiaonan Yang, Peichen Xie, Yizhi Liu, and Haotian Lin. Application of artificial intelligence in anterior segment ophthalmic diseases: diversity and standardization. Annals of Translational Medicine, 8(11), 2020. DOI: https://doi.org/10.21037/atm-20-976

LY Wong, EYK Ng, JS Suri, et al. Automatic identifica- tion of anterior segment eye abnormality. Irbm, 28(1):35– 41, 2007.

K.B. Ojha. Cataract-detection-using-cnn.: https://github.com/krishnabojha/Cataract-Detection- using-CNN, Accessed: July, 2023.

Xingzhi Qian, Evan W Patton, Justin Swaney, Qian Xing, and Tingying Zeng. Machine learning on cataracts classification using squeezenet. In 2018 4th international conference on universal village (UV), pages 1–3. IEEE, 2018. DOI: https://doi.org/10.1109/UV.2018.8642133

B Ramesh Kumar and MP Shimna. Recent approaches for automatic cataract detection analysis using image processing. Journal of Network Communications and Emerging Technologies (JNCET), 7(10), 2017.

Xinting Gao, Huiqi Li, Joo Hwee Lim, and Tien Yin Wong. Computer-aided cataract detection using enhanced texture features on retro-illumination lens images. In 2011 18th IEEE International Conference on Image Processing, pages 1565–1568. IEEE, 2011.

Meimei Yang, Ji-Jiang Yang, Qinyan Zhang, Yu Niu, and Jianqiang Li. Classification of retinal image for automatic cataract detection. In 2013 IEEE 15th International Conference on e-Health Networking, Applications and Services (Healthcom 2013), pages 674–679. IEEE, 2013. DOI: https://doi.org/10.1109/HealthCom.2013.6720761

Xinting Gao, Stephen Lin, and Tien Yin Wong. Auto- matic feature learning to grade nuclear cataracts based on deep learning. IEEE Transactions on Biomedical Engineering, 62(11):2693–2701, 2015. DOI: https://doi.org/10.1109/TBME.2015.2444389

Linglin Zhang, Jianqiang Li, He Han, Bo Liu, Jijiang Yang, Qing Wang, et al. Automatic cataract detection and grading using deep convolutional neural network. In 2017 IEEE 14th international conference on networking, sensing and control (ICNSC), pages 60–65. IEEE, 2017. DOI: https://doi.org/10.1109/ICNSC.2017.8000068

M Sahana and S Gowrishankar. Identification and classification of cataract stages in old age people using deep learning algorithm. International Journal of Innova- tive Technology and Exploring Engineering, 8(10):2767– 2772, 2019. DOI: https://doi.org/10.35940/ijitee.J9582.0881019

LY Wong, EYK Ng, JS Suri, et al. Automatic iden- tification of anterior segment eye abnormality. IRBM, ,28(1):35–41, 2007. DOI: https://doi.org/10.1016/j.rbmret.2007.02.002

Hadeer RM Tawfik, Rania AK Birry, and Amani A Saad. Early recogni- tion and grading of cataract using a combined log gabor/discrete wavelet trans- form with ann and svm. International Journal of Computer and Information Engineering, 12(12):1038–1043, 2018.

WMK Wan Mohd Khairosfaizal and AJ Nor’aini. Eyes detection in facial images using circular hough transform. In 2009 5th International Colloquium on Signal Processing & Its Applications, pages 238–242. IEEE, 2009. DOI: https://doi.org/10.1109/CSPA.2009.5069224

RA Ramlee, AR Ramli, and ZM Noh. Pupil segmentation of abnormal eye using image enhancement in spatial domain. In IOP Conference Series: Ma- terials Science and Engineering, volume 210, page 012031. IOP Publishing, 2017. DOI: https://doi.org/10.1088/1757-899X/210/1/012031

Mahesh Kumar SV. Computer-aided diagnosis of anterior segment eye abnormalities using visible wavelength image analysis-based machine learning. Journal of medical systems, 42(7):128, 2018. DOI: https://doi.org/10.1007/s10916-018-0980-z

Downloads

Published

22-03-2024

How to Cite

1.
Senthil Prakash PN, Sudharson S, Woonna VA, Teja Bacham SV. Effective Cataract Identification System using Deep Convolution Neural Network. EAI Endorsed Trans Perv Health Tech [Internet]. 2024 Mar. 22 [cited 2024 Apr. 25];10. Available from: https://publications.eai.eu/index.php/phat/article/view/5525