Eye Disease Detection in Retinal Images using Deep Transfer Learning Techniques
Main Article Content
Abstract
Image processing with the help of machine learning algorithms is breaking barriers in various fields of study, especially in the medical field. Deep learning image classification algorithms have made disease detection based on images in an easy manner thus assists the medical professional to taking quick decisions. This paper discusses various deep learning algorithms and transfer learning methods used to classify various eye diseases. Kaggle Eye dataset is used for this purpose. We compared four deep learning algorithms, namely EfficientNetB3, Inception V3, VGG 19 and Convolutional Neural Network models. Various categories of eye diseases, namely Cataract, Diabetic Retinopathy, Glaucoma are considered for classification with normal eye with the help of the scanned images. The strengths and weaknesses of these models are compared based on Precision, Recall, Accuracy and F1 score. In an identical testing environment, EfficientNet B3 outperforms the other algorithms and provides better accuracy for the classification of eye diseases.
Article Details
Upon receipt of accepted manuscripts, authors will be invited to complete a copyright license to publish the paper. At least the corresponding author must send the copyright form signed for publication. It is a condition of publication that authors grant an exclusive licence to the the INFOCOMP Journal of Computer Science. This ensures that requests from third parties to reproduce articles are handled efficiently and consistently and will also allow the article to be as widely disseminated as possible. In assigning the copyright license, authors may use their own material in other publications and ensure that the INFOCOMP Journal of Computer Science is acknowledged as the original publication place.
References
Abràmoff, M. D., Lou, Y., Erginay, A., Clarida, W., Amelon, R., Folk, J. C., and Niemeijer, M. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Investigative ophthalmology & visual science, 57(13):5200–5206, 2016.
Baldassarre, F., Morín, D. G., and Rodés-Guirao, L. Deep koalarization: Image colorization using cnns and inception-resnet-v2. arXiv preprint arXiv:1712.03400, 2017.
Bansal, M., Kumar, M., Sachdeva, M., and Mittal, A. Transfer learning for image classification using vgg19: Caltech-101 image data set. Journal of ambient intelligence and humanized computing, pages 1–12, 2021.
Bansal, M., Kumar, M., Sachdeva, M., and Mittal, A. Transfer learning for image classification using vgg19: Caltech-101 image data set. Journal of ambient intelligence and humanized computing, pages 1–12, 2021.
Carvalho, T., De Rezende, E. R., Alves, M. T., Balieiro, F. K., and Sovat, R. B. Exposing computer
generated images by eyeâs region classification via transfer learning of vgg19 cnn. In 2017 16th IEEE international conference on machine learning and applications (ICMLA), pages 866–870. IEEE, 2017.
Cheng, J., Tian, S., Yu, L., Gao, C., Kang, X.,Ma, X., Wu, W., Liu, S., and Lu, H. Resganet:Residual group attention network for medical imageclassification and segmentation. Medical ImageAnalysis, 76:102313, 2022.
Dey, N., Zhang, Y.-D., Rajinikanth, V., Pugalenthi,R., and Raja, N. S. M. Customized vgg19 architecture for pneumonia detection in chest xrays. Pattern Recognition Letters, 143:67–74, 2021.
Dong, N., Zhao, L., Wu, C.-H., and Chang, J.- F. Inception v3 based cervical cell classification combined with artificially extracted features. Applied Soft Computing, 93:106311, 2020.
dos Santos, M. R., Batista, A. P., Rosa, R. L., Saadi, M., Melgarejo, D. C., and Rodríguez, D. Z. Asqm: Audio streaming quality metric
based on network impairments and user preferences. IEEE Transactions on Consumer Electronics, 69(3):408–420, 2023.
Dos Santos, M. R., Rodriguez, D. Z., and Rosa, R. L. A novel qoe indicator for mobile networks
based on twitter opinion ranking. In 2023 International Conference on Software, Telecommunications
and Computer Networks (SoftCOM), pages 1–6. IEEE, 2023.
Gaur, L., Bhatia, U., Jhanjhi, N., Muhammad, G., and Masud, M. Medical image-based detection of
covid-19 using deep convolution neural networks. Multimedia systems, 29(3):1729–1738, 2023.
Granger, E., Kiran, M., Blais-Morin, L.-A., et al. A comparison of cnn-based face and head detectors
for real-time video surveillance applications. In 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA), pages 1–7. IEEE, 2017.
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in
retinal fundus photographs. jama, 316(22):2402–2410, 2016.
Jain, L., Murthy, H. S., Patel, C., and Bansal, D. Retinal eye disease detection using deep learning. In 2018 Fourteenth International Conference on Information Processing (ICINPRO), pages 1–6. IEEE, 2018.
Kamble, R. M., Chan, G. C., Perdomo, O., Kokare, M., Gonzalez, F. A., Müller, H., and Mériaudeau, F. Automated diabetic macular edema (dme) analysis using fine tuning with inception-resnet-v2 on oct images. In 2018 IEEEEMBS
Conference on Biomedical Engineering and Sciences (IECBES), pages 442–446. IEEE, 2018.
Kaur, M. and Kamra, A. Detection of retinal abnormalities in fundus image using transfer learning
networks. Soft Computing, 27(6):3411–3425, 2023. INFOCOMP, v. 23, no. 1, p. pp-pp, June, 2024.
Kim, H. E., Cosa-Linan, A., Santhanam, N., Jannesari, M., Maros, M. E., and Ganslandt, T. Transfer
learning for medical image classification: a literature review. BMC medical imaging, 22(1):69, 2022.
Kim, H. E., Cosa-Linan, A., Santhanam, N., Jannesari, M., Maros, M. E., and Ganslandt, T. Transfer
learning for medical image classification: a literature review. BMC medical imaging, 22(1):69, 2022.
Koonce, B. and Koonce, B. Resnet 50. Convolutional Neural Networks with Swift for Tensorflow:
Image Recognition and Dataset Categorization, pages 63–72, 2021.
Kora, P., Ooi, C. P., Faust, O., Raghavendra, U., Gudigar, A., Chan, W. Y., Meenakshi, K.,
Swaraja, K., Plawiak, P., and Acharya, U. R. Transfer learning techniques for medical image
analysis: A review. Biocybernetics and Biomedical Engineering, 42(1):79–107, 2022.
Krueangsai, A. and Supratid, S. Effects of shortcut-level amount in lightweight resnet of
resnet on object recognition with distinct number of categories. In 2022 International Electrical Engineering
Congress (iEECON), pages 1–4. IEEE, 2022.
Kumar, S., Pathak, S., and Kumar, B. Automated detection of eye related diseases using digital image
processing. Handbook of multimedia information security: techniques and applications, pages 513–544, 2019.
Liu, Q., Yu, L., Luo, L., Dou, Q., and Heng, P. A. Semi-supervised medical image classification
with relation-driven self-ensembling model. IEEE transactions on medical imaging, 39(11):3429–3440, 2020.
Miranda, E., Aryuni, M., and Irwansyah, E. A survey of medical image classification techniques.
In 2016 international conference on information management and technology (ICIMTech), pages 56–61. IEEE, 2016.
Nagpal, D., Alsubaie, N., Soufiene, B. O., Alqahtani, M. S., Abbas, M., and Almohiy, H. M. Automatic
detection of diabetic hypertensive retinopathy in fundus images using transfer learning. Applied Sciences, 13(8):4695, 2023.
Okey, O. D., Udo, E. U., Rosa, R. L., Rodríguez, D. Z., and Kleinschmidt, J. H. Investigating chatgpt
and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers &Security, 135:103476, 2023.
Prasad, K., Sajith, P., Neema, M., Madhu, L., and Priya, P. Multiple eye disease detection using deep
neural network. In TENCON 2019-2019 IEEE Region 10 Conference (TENCON), pages 2148– 2153. IEEE, 2019.
Rajinikanth, V., Joseph Raj, A. N., Thanaraj, K. P., and Naik, G. R. A customized vgg19 network
with concatenation of deep and handcrafted features for brain tumor detection. Applied Sciences, 10(10):3429, 2020.
Ravudu, M., Jain, V., and Kunda, M. M. R. Review of image processing techniques for automatic
detection of eye diseases. In 2012 Sixth International Conference on Sensing Technology (ICST), pages 320–325. IEEE, 2012.
Reddy, A. S. B. and Juliet, D. S. Transfer learning with resnet-50 for malaria cell-image classification.
In 2019 International Conference on Communication and Signal Processing (ICCSP), pages 0945–0949. IEEE, 2019.
Rezaie, V., Parnianifard, A., Rodriguez, D., Mumtaz, S., and Wuttisittikulkij, L. Speech emotion recognition using anfis and pso-optimization with word2vec. J Neuro Spine, 1(1):41–56, 2023.
Ribani, R. and Marengoni, M. A survey of transfer learning for convolutional neural networks.
In 2019 32nd SIBGRAPI conference on graphics, patterns and images tutorials (SIBGRAPI-T), pages 47–57. IEEE, 2019.
Sarki, R., Ahmed, K., Wang, H., Zhang, Y., Ma, J., and Wang, K. Image preprocessing in classification
and identification of diabetic eye diseases. Data Science and Engineering, 6(4):455–471, 2021.
Sarwinda, D., Paradisa, R. H., Bustamam, A., and Anggia, P. Deep learning in image classification
using residual network (resnet) variants for detection of colorectal cancer. Procedia Computer Science, 179:423–431, 2021.
Sharma, H., Wasim, J., and Sharma, P. Analysis of eye disease classification by fundus images using
different machine/deep/transfer learning techniques. In 2024 4th International Conference on
Innovative Practices in Technology and Management (ICIPTM), pages 1–6. IEEE, 2024.
Silva, D. H., Maziero, E. G., Saadi, M., Rosa, R. L., Silva, J. C., Rodriguez, D. Z., and Igorevich,
K. K. Big data analytics for critical information classification in online social networks using classifier chains. Peer-to-Peer Networking and Applications, 15(1):626–641, 2022.
Srivastava, S., Divekar, A. V., Anilkumar, C., Naik, I., Kulkarni, V., and Pattabiraman, V. Comparative
analysis of deep learning image detection algorithms. Journal of Big data, 8(1):1–27, 2021.
Srivastava, S., Divekar, A. V., Anilkumar, C., Naik, I., Kulkarni, V., and Pattabiraman, V. Comparative
analysis of deep learning image detection algorithms. Journal of Big data, 8(1):1–27, 2021.
Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. Inception-v4, inception-resnet and the impact
of residual connections on learning. In Proceedings of the AAAI conference on artificial intelligence, volume 31, 2017.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. Rethinking the inception architecture
for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818–2826, 2016.
Tan, M. and Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks.
In International conference on machine learning, pages 6105–6114. PMLR, 2019.
Wang, C., Chen, D., Hao, L., Liu, X., Zeng, Y., Chen, J., and Zhang, G. Pulmonary image classification
based on inception-v3 transfer learning model. IEEE Access, 7:146533–146541, 2019.
Wang, J., He, X., Faming, S., Lu, G., Cong, H.,
and Jiang, Q. A real-time bridge crack detection
method based on an improved inception-resnet-v2
structure. IEEE Access, 9:93209–93223, 2021.
Wang, M. and Gong, X. Metastatic cancer image
binary classification based on resnet model. In
IEEE 20th international conference on communication
technology (ICCT), pages 1356–1359.
IEEE, 2020.
Wang,W., Liang, D., Chen, Q., Iwamoto, Y., Han,
X.-H., Zhang, Q., Hu, H., Lin, L., and Chen, Y.-
W. Medical image classification using deep learning.
Deep learning in healthcare: paradigms and
applications, pages 33–51, 2020.
Xia, X., Xu, C., and Nan, B. Inception-v3
for flower classification. In 2017 2nd international
conference on image, vision and computing
(ICIVC), pages 783–787. IEEE, 2017.
Yadav, S. S. and Jadhav, S. M. Deep convolutional
neural network based medical image classification
for disease diagnosis. Journal of Big data, 6(1):1–
, 2019.
Zakaria, N., Mohamed, F., Abdelghani, R., and
Sundaraj, K. Three resnet deep learning architectures
applied in pulmonary pathologies classification.
In 2021 International Conference on Artificial
Intelligence for Cyber Security Systems and
Privacy (AI-CSP), pages 1–8. IEEE, 2021.
Zhang, Q., Bai, C., Liu, Z., Yang, L. T., Yu,
H., Zhao, J., and Yuan, H. A gpu-based residual
network for medical image classification in
smart medicine. Information Sciences, 536:91–
, 2020.
Zhou, Y., Li, G., and Li, H. Automatic cataract
classification using deep neural network with discrete
state transition. IEEE transactions on medical
imaging, 39(2):436–446, 2019.
Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu,
H., Xiong, H., and He, Q. A comprehensive survey
on transfer learning. Proceedings of the IEEE,
(1):43–76, 2020.