A Novel Approach for Early Recognition of Cataract using VGG-16 and Custom User-based Region of Interest
Pages 15 - 18
Abstract
Cataract is a severe eye disease and is one of the main causes of vision impairment across the world. There have been many researches about cataract prediction using machine and deep learning state-of-the-art methods but all of these methods rely on automatic image classification without using the knowledge of the clinicians. This study proposes a novel approach that incorporates artificial intelligence with the human intelligence of experienced medical professionals. Our model allows manual selection of the region in the fundus image that is most likely to have cataract and gives it more importance as compared to the rest of the region in the image. After that, a VGG-16 model was applied to classify the input images. Accuracy was chosen as the performance metric and the maximum accuracy observed for the model was 98.83%.
References
[1]
Edupuganti, V.G., Chawla, A., Kale, A., 2018. Automatic optic disk and cup segmentation of fundus images using deep learning, in: 2018 IEEE International Conference on Image Processing, ICIP 2018, Athens, Greece, October 7-10, 2018, IEEE. pp. 2227–2231.URL: https://doi.org/10.1109/ICIP.2018.8451753.
[2]
Linglin Zhang, "Automatic cataract detection and grading using Deep Convolutional Neural Network," 2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC), 2017, pp. 60-65.
[3]
Junjun He, Cheng Li, Jin Ye, Yu Qiao, Lixu Gu,Multi-label ocular disease classification with a dense correlation deep neural network,Biomedical Signal Processing and Control,Volume 63,2021,102167,ISSN 1746-8094,https://doi.org/10.1016/j.bspc.2020.102167.(https://www.sciencedirect.com/science/article/pii/S1746809420303062)
[4]
Chorage, S., Khot, S.S.: Detection of diabetic retinopathy and cataract by vessel extraction from fundus images. In: 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA), vol. 1, IEEE, pp. 638–641 (2017)
[5]
O. Russakovsky, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis., vol. 115, no. 3, pp. 211–252, 2015.
[6]
Fan,W., Shen, R., Zhang, Q.,Yang, J.J., Li, J.: Principal component analysis based cataract grading and classification. In: 2015 17th International Conference on E-health Networking, Application and Services (HealthCom), IEEE, pp. 459–462 (2015)
[7]
Güven, A.: Automatic detection of age-related macular degeneration pathologies in retinal fundus images. Comput. Methods Biomech. Biomed. Eng. 16(4), 425–434 (2013)
[8]
Nayak, J.,Acharya,R., Bhat, P.S., Shetty,N., Lim, T.C.:Automated diagnosis of glaucoma using digital fundus images. J. Med. Syst.33(5), 337 (2009)
[9]
Souza, M.B.,Medeiros, F.W., Souza, D.B.,Garcia, R.,Alves,M.R.:Evaluation of machine learning classifiers in keratoconus detection from orbscan ii examinations. Clinics 65(12), 1223–1228 (2010)
[10]
Wang W, Han X, Zhang J, Br J Ophthalmol Epub ahead of print: [please include Day Month Year]. bjophthalmol-2020-318609
[11]
A. Soni and A. Rai, "A Novel Approach for the Early Recognition of Diabetic Retinopathy using Machine Learning," 2021 International Conference on Computer Communication and Informatics (ICCCI), 2021, pp. 1-5.
[12]
Imran, A., Li, J., Pei, Y. Fundus image-based cataract classification using a hybrid convolutional and recurrent neural network. Vis Comput 37, 2407–2417 (2021). https://doi.org/10.1007/s00371-020-01994-3
[13]
V. Nair, S. Suranglikar, S. Deshmukh and Y. Gavhane, "Multi-labelled Ocular Disease Diagnosis Enforcing Transfer Learning," 2021 55th Annual Conference on Information Sciences and Systems (CISS), 2021, pp. 1-6.
[14]
Ji-Jiang Yang, Jianqiang Li, Ruifang Shen, Yang Zeng, Jian He, Jing Bi, Yong Li, Qinyan Zhang, Lihui Peng, Qing Wang, Exploiting ensemble learning for automatic cataract detection and grading, Computer Methods and Programs in Biomedicine, Volume 124, 2016, Pages 45-57, ISSN 0169-2607, https://doi.org/10.1016/j.cmpb.2015.10.007.
[15]
Neha Gour, Pritee Khanna, Multi-class multi-label ophthalmological disease detection using transfer learning based convolutional neural network, Biomedical Signal Processing and Control, Volume 66, 2021, 102329, ISSN 1746-8094, https://doi.org/10.1016/j.bspc.2020.102329.(https://www.sciencedirect.com/science/article/pii/S1746809420304432)
[16]
M. S. Junayed, M. B. Islam, A. Sadeghzadeh and S. Rahman, "CataractNet: An Automated Cataract Detection System Using Deep Learning for Fundus Images," in IEEE Access, vol. 9, pp. 128799-128808, 2021.
[17]
Behera, Manoj & Chakravarty, Sujata & Gourav, Apurwa & Dash, Satyabrata. (2020). Detection of Nuclear Cataract in Retinal Fundus Image using RadialBasis FunctionbasedSVM. 278-281. 10.1109/PDGC50313.2020.9315834.
[18]
SungMin Suh, Yongeun Park, KyoungMin Ko, SeongMin Yang, Jaehyeong Ahn, Jae-Ki Shin, SungHwan Kim, "Weighted Mask R-CNN for Improving Adjacent Boundary Segmentation", Journal of Sensors, vol. 2021, Article ID 8872947, 8 pages, 2021. https://doi.org/10.1155/2021/8872947
[19]
Tao Li, Wang Bo, Chunyu Hu, Hong Kang, Hanruo Liu, Kai Wang, Huazhu Fu, Applications of deep learning in fundus images: A review, Medical Image Analysis, Volume 69, 2021,101971, ISSN 1361-8415,https://doi.org/10.1016/j.media.2021.101971.(https://www.sciencedirect.com/science/article/pii/S1361841521000177)
Recommendations
A 3D CNN-based Multi-task Learning for Cataract screening and left and right eye classification on 3D AS-OCT images
ICIMH '21: Proceedings of the 2021 International Conference on Intelligent Medicine and HealthCataract is the leading cause for visual impairment and blindness. Cataract screening can effectively improve the recovery rate of cataract, and the left and right eye classification is a significant step in cataract screening. Anterior segment optical ...
Transductive Instance Transfer Learning for Cross-Language Defect Prediction
APIT '22: Proceedings of the 2022 4th Asia Pacific Information Technology ConferencePredicting defects (bugs) is critical to increasing software quality. Many software defect prediction algorithms have been presented, and many of them have shown to be effective in practice. However, because existing works are largely limited to a ...
Decoding the Brain Waves using EEG signals for classifying Body Gestures by applying suitable ML & DL Techniques
APIT '22: Proceedings of the 2022 4th Asia Pacific Information Technology ConferenceThis work aims to use EEG brain signals captured from the scalp of subjects at 500 Hz for classification of grasping tasks which is useful for BCI (Brain-Computer Interface) and comparative analysis of appropriate Machine Learning and Deep Learning ...
Comments
Information & Contributors
Information
Published In
January 2022
239 pages
ISBN:9781450395571
DOI:10.1145/3512353
Copyright © 2022 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 14 March 2022
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
APIT 2022
APIT 2022: 2022 4th Asia Pacific Information Technology Conference
January 14 - 16, 2022
Virtual Event, Thailand
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 89Total Downloads
- Downloads (Last 12 months)6
- Downloads (Last 6 weeks)0
Reflects downloads up to 03 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format