An Automated Wavelet Scattering Network Classification Using Three Stages of Cataract Disease
DOI:
https://doi.org/10.21123/bsj.2024.8995Keywords:
Cataract disease, Cataract category, Deep learning network, Retinal fundus images, wavelet scattering.Abstract
A cataract is an eye disease that causes visual distortion and the late stage of this disease can lead to blindness. It is considered a silent disease that can occur without the appearance of symptoms. Therefore, the most effective way to detect cataracts is through accurate and timely detection to prevent hurting, expensive operations, and to stop blindness. The purpose of this paper is to propose an automated system based on the wavelet scattering network which categorizes the patients into four classes: early cataract, intermediate cataract, late cataract, and non-cataract conditions using 512 images of the ODIR dataset (212 Cataract and 300 of Normal). The first step in this technique is the preprocessing step for the retinal image was a mean filter, which was utilized to reduce the image's noise. The limited contrast adaptive histogram equalization (CLAHE) method was then employed to improve the image's contract level. Then, Low-variance characteristics can be extracted from image data using a wavelet scattering network for use in deep learning applications. In this network, lowpass scaling filters and predefined wavelets are employed. The average accuracy of the suggested method was 100% for four classes (Normal, Early, Moderate, and Severe). The results are promising compared with other similar works.
Received 25/04/2023
Revised 04/08/2023
Accepted 06/08/2023
Published Online First 20/02/2024
References
El Abbadi N, Al Saadi E. Blood Vessel Diameter Measurement on Retinal Image. J Comput Sci. 2014; 10 (5): 879-883. https://doi.org/10.3844/jcssp.2014.879.883
El Abbadi N, Al Saadi E. Improvement of Automatic Hemorrhages Detection Methods using Shapes Recognition. J Comput Sci. 2013; 9 (9): 1205-1210. https://doi.org/10.3844/jcssp.2013.1205.1210
Bushra A, Nur H, Nor H. A Comprehensive Review on Medical Image Steganography Based on LSB Technique and Potential Challenges. Baghdad Sci J. 2021; 18(2): 957-974. https://doi.org/10.21123/bsj.2021.18.2(Suppl.).0957
Mosa Z, Ghae N, Ali A. Detecting Kkeratoconus by using SVM and Decision Tree Classifiers with the Aid of Image Processing. Baghdad Sci J. 2019; 16(4): 1022–1029. https://doi.org/10.21123/bsj.2019.16.4(Suppl.).1022
Mona N. Automated Cataract Grading using Smartphone Images. Ontario. Canada: Waterloo University Press; 2020.
Behnam A, Peter H, Jo W. Detecting Cataract Using Smartphones. IEEE J Transl Eng Health Med. 2021; 9(3800110): 1-10.
Aditya K, Jai K, Hetal M, Winfried A. Cataract surgery in diabetes mellitus: A systematic review. Indian J Ophthalmol. 2018 Oct; 66(10): 1401–1410. https://doi.org/10.4103/ijo.IJO_1158_17
Heidari M, Mirniaharikandehei S, Khuzani A, Danala G, Qiu Y, Zheng B. Improving the Performance of CNN to Predict the Likelihood of COVID-19 using Chest X-ray Images with Preprocessing Algorithms. Int J Med Inform. 2020; 144 (104284): 1-9. https://doi.org/10.1016/j.ijmedinf.2020.104284
Ki S, Jongwoo K, Eunseok K, Si L, Min-Ji K, Jisang H. Deep Learning-Based Cataract Detection and Grading from Slit-Lamp and Retro-Illumination Photographs: Model Development and Validatio Study. Ophthalmol Sci. 2022 June; 2 (2): 1-9. https://doi.org/10.1016/j.xops.2022.100147
Turimerla P, Priyanka K. Computer-aided Diagnosis of Cataract using Deep Transfer Learning. Biomed Signal Process Control. 2019 Aug; 53(5): 101533. https://doi.org/10.1016/j.bspc.2019.04.010
Jing W, Liu Y, Zhanqiang H, Weifeng H, Junwei L. Multi-Label Classification of Fundus Images With EfficientNet. IEEE Access. 2020; 8 (20163157): 212499–212508. https://doi.org/10.1109/ACCESS.2020.3040275
Jayachitra S, Kanna K, Pavithra G, Ranjeetha T. A Novel Eye Cataract Diagnosis and Classification Using Deep Neural Network. J Phys Conf Ser. 2021;1937(1):1-6. https://doi.org/10.1088/1742-6596/1937/1/012053
Junayed M, Islam M, Sadeghzadeh A, Rahman S. CataractNet: An Automated Cataract Detection System Using Deep Learning for Fundus Images. IEEE Access. 2021; 9(21100618): 128799-128808. https://doi.org/10.1109/ACCESS.2021.3112938
Kamrul H, Tanjum T, Ruhul A, Omar F, Mohammad M, Sultan A, et al. Cataract Disease Detection by Using Transfer Learning-Based Intelligent Methods. Comput Math Methods Med. 2021; 2021 (7666365): 1-11. https://doi.org/10.1155/2021/7666365
Hind H, Ali Y, Enas H. Classifying Three Stages of Cataract Disease using CNN. J Univ Babylon Pure Appl Sci. 2022; 30(3): 150-167.
Richard B, Yunendah F, Rita M, Sofia S, Abel B, Ibnu D. Cataract Classification Based on Fundus Images Using Convolutional Neural Network. Int J Inform Vis. 2022 Mar; 6(1): 33-38. https://doi.org/10.30630/joiv.6.1.856
Yaroub E. Cataract grading method based on deep convolutional neural networks and stacking ensemble learning. Int J Imaging Syst Technol. 2022; 32 (3): 798-814.
Bruna J, Mallat S. Invariant Scattering Convolution Networks. IEEE Trans Pattern Anal Mach Intell. 2013; 35(8): 1872-1886. https://doi.org/10.1109/TPAMI.2012.230
And´en J, Mallat S. Deep Scattering Spectrum. IEEE Trans Signal Process. 2014; 62(16): 4114–4128. https://doi.org/10.1109/TSP.2014.2326991
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