Enhancing Retinal Scan Classification: A Comparative Study of Transfer Learning and Ensemble Techniques

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Published: Jul 13, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i7s.7031
Keywords:
Deep Learning, Ensemble Learning, EfficientNetV2M, Retinal scans, Transfer Learning

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Ajitkumar Shitole
Associate Professor – Department of Computer Engineering, Hope Foundation’s International Institute of Information Technology, Pune, India
Aryan Kenchappagol
Department of Computer Engineering, Hope Foundation’s International Institute of Information Technology, Pune, India
Rutuja Jangle
Department of Computer Engineering, Hope Foundation’s International Institute of Information Technology, Pune, India
Yashowardhan Shinde
Department of Computer Engineering, Hope Foundation’s International Institute of Information Technology, Pune, India
Akalbir Singh Chadha
Department of Computer Engineering, Hope Foundation’s International Institute of Information Technology, Pune, India

Abstract

Ophthalmic diseases are a significant health concern globally, causing visual impairment and blindness in millions of people, particularly in dispersed populations. Among these diseases, retinal fundus diseases are a leading cause of irreversible vision loss, and early diagnosis and treatment can prevent this outcome. Retinal fundus scans have become an indispensable tool for doctors to diagnose multiple ocular diseases simultaneously. In this paper, the results of a variety of deep learning models (DenseNet-201, ResNet125V2, XceptionNet, EfficientNet-B7, MobileNetV2, and EfficientNetV2M) and ensemble learning approaches are presented, which can accurately detect 20 common fundus diseases by analyzing retinal fundus scan images. The proposed model is able to achieve a remarkable accuracy of 96.98% for risk classification and 76.92% for multi-disease detection, demonstrating its potential for use in clinical settings. By utilizing the proposed model, doctors can provide swift and accurate diagnoses to patients, improving their chances of receiving timely treatment and preserving their vision.

Article Details

How to Cite
Shitole, A. ., Kenchappagol, A. ., Jangle, R. ., Shinde, Y. ., & Chadha, A. S. . (2023). Enhancing Retinal Scan Classification: A Comparative Study of Transfer Learning and Ensemble Techniques. International Journal on Recent and Innovation Trends in Computing and Communication, 11(7s), 520–528. https://doi.org/10.17762/ijritcc.v11i7s.7031
Issue
Vol. 11 No. 7s (2023)
Section
Articles

References

Gu, H., Guo, Y., Gu, L. et al. Deep learning for identifying corneal diseases from ocular surface slit-lamp photographs. Sci Rep 10, 17851 (2020). https://doi.org/10.1038/s41598-020-75027-3

Cen, LP., Ji, J., Lin, JW. et al. Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks. Nat Commun 12, 4828 (2021). https://doi.org/10.1038/s41467-021-25138-w

Nuzzi R, Boscia G, Marolo P and Ricardi F (2021) The Impact of Artificial Intelligence and Deep Learning in Eye Diseases: A Review. Front. Med. 8:710329. doi: 10.3389/fmed.2021.710329

Md Shakib Khan, Nafisa Tafshir, Kazi Nabiul Alam, Abdur Rab Dhruba, Mohammad Monirujjaman Khan, Amani Abdulrahman Albraikan, Faris A. Almalki, "Deep Learning for Ocular Disease Recognition: An Inner-Class Balance", Computational Intelligence and Neuroscience, vol. 2022, Article ID 5007111, 12 pages, 2022. https://doi.org/10.1155/2022/5007111

Ting DSW, Pasquale LR, Peng L, et alArtificial intelligence and deep learning in ophthalmologyBritish Journal of Ophthalmology 2019;103:167-175.

Tan, C.; Sun, F.; Kong, T.; Zhang, W.; Yang, C. & Liu, C. (2018), 'A Survey on Deep Transfer Learning' , cite arxiv:1808.01974Comment: The 27th International Conference on Artificial Neural Networks (ICANN 2018)

Wang, K.,Gao, X.,Zhao, Y.,et al. Pay Attention to Features, Transfer Learn Faster CNNsC],2020.

Rezaei, Shahbaz & Liu, Xin. (2019). A Target-Agnostic Attack on Deep Models: Exploiting Security Vulnerabilities of Transfer Learning.

Ammar Mohammed, Rania Kora, A comprehensive review on ensemble deep learning: Opportunities and challenges, Journal of King Saud University - Computer and Information Sciences, Volume 35, Issue 2, 2023, Pages 757-774, ISSN 1319-1578, https://doi.org/10.1016/j.jksuci.2023.01.014.

Su, K.; Wu, J.; Gu, D.; Yang, S.; Deng, S.; Khakimova, A.K. An Adaptive Deep Ensemble Learning Method for Dynamic Evolving Diagnostic Task Scenarios. Diagnostics 2021, 11, 2288. https://doi.org/10.3390/diagnostics11122288

Müller, Dominik & Soto-Rey, Iñaki & Kramer, Frank. (2022). An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural Networks.

E. TaSci and A. Ugur, "Image classification using ensemble algorithms with deep learning and hand-crafted features," 2018 26th Signal Processing and Communications Applications Conference (SIU), Izmir, Turkey, 2018, pp. 1-4, doi: 10.1109/SIU.2018.8404179.

Naseer I, Akram S, Masood T, Jaffar A, Khan MA, Mosavi A. Performance Analysis of State-of-the-Art CNN Architectures for LUNA16. Sensors (Basel). 2022 Jun 11;22(12):4426. doi: 10.3390/s22124426. PMID: 35746208; PMCID: PMC9227226.

A. A. Ardakani, A. R. Kanafi, U. R. Acharya, N. Khadem, and A. Mohammadi, “Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: results of 10 convolutional neural networks,” Computers in Biology and Medicine, vol. 121, article 103795, 2020.

Porag, Al Mohidur Rahman, Md. Mahedi Hasan and Dr. Md Taimur Ahad. “A Comparison Study of Deep CNN Architecture in Detecting Pneumonia.” ArXiv abs/2212.14744 (2022): n. pag.

K. Prasad, P. S. Sajith, M. Neema, L. Madhu and P. N. Priya, "Multiple eye disease detection using Deep Neural Network," TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON), Kochi, India, 2019, pp. 2148-2153, doi: 10.1109/TENCON.2019.8929666

Topaloglu, Ismail. (2022). Deep Learning Based Convolutional Neural Network Structured New Image Classification Approach for Eye Disease Identification. Scientia Iranica. 10.24200/SCI.2022.58049.5537.

Nazir, Tahira, Aun Irtaza, Ali Javed, Hafiz Malik, Dildar Hussain, and Rizwan Ali Naqvi. 2020. "Retinal Image Analysis for Diabetes-Based Eye Disease Detection Using Deep Learning," Applied Sciences 10, no. 18: 6185. https://doi.org/10.3390/app10186185

Liu, J., Deng, Y., Wang, J., et al. (2020). Automated detection and classification of four common types of ocular diseases using deep learning. Frontiers in Bioengineering and Biotechnology, 8, 62.

Rajalakshmi, R., Subashini, R., Arunprasath, V., et al. (2018). Performance of deep learning algorithms for detection of diabetic retinopathy using retinal images: A systematic review and meta-analysis. Eye, 32, 1138-1158.

Zebin, T., Minaee, S., Abdolrahimzadeh, S., et al. (2020). Early detection of ocular diseases using deep learning on retinal OCT images. Medical Image Analysis, 65, 101765.

Samiksha Pachade, Prasanna Porwal, Dhanshree Thulkar, Manesh Kokare, Girish Deshmukh, Vivek Sahasrabuddhe, Luca Giancardo, Gwenolé Quellec, Fabrice Mériaudeau, November 25, 2020, "Retinal Fundus Multi-disease Image Dataset (RFMiD)", IEEE Dataport, doi: https://dx.doi.org/10.21227/s3g7-st65.

Marcus D Bloice, Peter M Roth, Andreas Holzinger, Biomedical image augmentation using Augmentor, Bioinformatics, Volume 35, Issue 21, 1 November 2019, Pages 4522–4524, https://doi.org/10.1093/bioinformatics/btz259

Wang, Shuihua and Yudong Zhang. “DenseNet-201-Based Deep Neural Network with Composite Learning Factor and Precomputation for Multiple Sclerosis Classification.” ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 16 (2020): 1 - 19.

Dr. Naveen Jain. (2020). Artificial Neural Network Models for Material Classification by Photon Scattering Analysis. International Journal of New Practices in Management and Engineering, 9(03), 01 - 04. https://doi.org/10.17762/ijnpme.v9i03.88

Nur Aziz Thohari, Afandi & Triyono, Liliek & Hestiningsih, Idhawati & Suyanto, Budi & Yobioktobera, Amran. (2022). Performance Evaluation of Pre-Trained Convolutional Neural Network Model for Skin Disease Classification. JUITA: Jurnal Informatika. 10. 9. 10.30595/juita.v10i1.12041.

D. Shah, D. Shah, D. Jodhawat, J. Parekh and K. Srivastava, "Xception Net & Vision Transformer: A comparative study for Deepfake Detection," 2022 International Conference on Machine Learning, Computer Systems and Security (MLCSS), Bhubaneswar, India, 2022, pp. 393-398, doi: 10.1109/MLCSS57186.2022.00077.

R. S. S. Devi, V. R. V. Kumar and P. Sivakumar, "Efficientnetv2 model for plant disease classification and pest recognition," Computer Systems Science and Engineering, vol. 45, no.2, pp. 2249–2263, 2023.

Tan, Mingxing & Le, Quoc. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.

Sandler, Mark & Howard, Andrew & Zhu, Menglong & Zhmoginov, Andrey & Chen, Liang-Chieh. (2018). MobileNetV2: Inverted Residuals and Linear Bottlenecks. 4510-4520. 10.1109/CVPR.2018.00474.

Paul Garcia, Ian Martin, Laura López, Sigurðsson Ólafur, Matti Virtanen. Automated Grading Systems: Advancements and Challenges. Kuwait Journal of Machine Learning, 2(1). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/165

Ajitkumar Shitole, Dr. Manoj Devare, “Optimization of IoT Enabled Physical Location Monitoring using DT and VAR”, International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), ISSN: 1557-3958, Vol: 15, Issue: 4, Oct 2022.