Identification of Glaucoma from Retinal Fundus Images using Deep Learning Model, MobileNet
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Abstract
Glaucoma is one of the leading causes of permanent vision impairment and blindness everywhere in the world due to high intraocular stress inside the eyes. So, early and accurate detection is crucial for preventing irreversible vision loss. Manual recognition of Glaucoma is a difficult task that requires proficiency and a highly experienced person. Computer Aided Detection (CAD) techniques assist ophthalmologists in the detection of such ophthalmologic diseases by analyzing the retinal fundus images. Deep learning(DL) algorithms have accomplished exceptionally in various computer vision applications by giving them an apparent advantage over more traditional techniques by analyzing the retinal fundus images. In this paper, a pre-trained Convolutional Neural Network, MobileNet, is used to detect Glaucoma in retinal fundus images by automatically extracting the features rather than manually classifying fundus images. Due to efficiency, speed of execution, accuracy, customizability, transfer learning, real-time applications and size of the architecture, MobileNet is proposed here. The DrishtiGS, EyePACS AIROGS-Light, BEH, REFUGE, sjchoi86- HRF, CRFO-v4, G1020, FIVES, and PAPILA datasets were utilized in this study. The suggested model is evaluated by significant factors such as accuracy, precision, sensitivity, specificity, F1 score, and Confusion Matrix to examine the model's efficacy. These studies reveal the capability of the DL approach to classify Glaucoma from retinal fundus images and recommend that the suggested approach can assist ophthalmologists in a quick, correct, and dependable diagnosis of Glaucoma.
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References
S. I. Khan, S. B. Choubey, A. Choubey, A. Bhatt, P. V. Naishadhkumar and M. M. Basha, “Automated Glaucoma Detection from Fundus Images Using Wavelet-Based Denoising and Machine Learning,” Concurrent Engineering, vol. 30, no. 1, pp. 103-115, 2021.
S. Ajitha, J. D. Akkara and M. V. Judy, “Identification of Glaucoma from fundus images using deep learning techniques,” Indian Journal of Ophthalmology, vol. 69, no. 10, pp. 2702-2709, 2021.
N. Akter, J. Fletcher, S. Perry, M. P. Simunovic, N. Briggs and M. Roy, “Glaucoma Diagnosis Using Multi-Feature Analysis and a Deep Learning Technique,” Scientific Reports, vol. 12, no. 8064, 2022.
R. Hemelings, B. Elen, J. Barbosa-Breda, M. B. Blaschko, P. De Boever and I. Stalmans, “Deep Learning on Fundus Images Detects Glaucoma beyond the Optic Disc,” Scientific Reports, vol. 11, no. 20313, 2021.
S. Joshi, B. Partibane, W. A. Hatamleh, H. Tarazi, C. S. Yadav and D. Krah, “Glaucoma Detection Using Image Processing and Supervised Learning for Classification,” Journal of Healthcare Engineering, vol. 2022, no. 2988262, 2022.
R. Kashyap, R. Nair, S. M. P. Gangadharan, M. Botto-Tobar, S. Farooq and A. Rizwan, “Glaucoma Detection and Classification Using Improved U-Net Deep Learning Model,” Healthcare, vol. 10, no. 12, p. 2497, 2022.
B. S. Kirar and D. K. Agrawal, “Current Research on Glaucoma Detection Using Compact Variational Mode Decomposition from Fundus Images,” International Journal of Intelligent Engineering and Systems, vol. 12, no. 3, pp. 1–10, 2019.
L. J. Coan, B. M. Williams, V. K. Adithya, S. Upadhyaya, A. Alkafri, S. Czanner, R. Venkatesh, C. E. Willoughby, S. Kavitha and G. Czanner, “Automatic detection of glaucoma via fundus imaging and artificial intelligence: A review,” Survey of Ophthalmology, vol. 68, no. 1, pp. 17-41, 2023.
Y. Harjoseputro, I. P. Yuda and K. P. Danukusumo, “MobileNets: Efficient Convolutional Neural Network for Identification of Protected Birds,” International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 6, pp. 2290-2296, 2020.
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto and H. Adam, “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” arXiv preprin arXiv:1704.04861, 2017.
L. Jia, T. Wang, Y. Chen, Y. Zang, X. Li, H. Shi and L. Gao, “MobileNet-CA-YOLO: An Improved YOLOv7 Based on the MobileNetV3 and Attention Mechanism for Rice Pests and Diseases Detection,” Agriculture, vol. 13, no. 7, pp. 1285–1285, 2023.
B. Khasoggi, E. Ermatita and S. Samsuryadi, “Efficient Mobilenet Architecture as Image Recognition on Mobile and Embedded Devices,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 16, no. 1, pp. 389-394, 2019.
A. Michele, V. Colin and D. D. Santika, “MobileNet Convolutional Neural Networks and Support Vector Machines for Palmprint Recognition,” Procedia Computer Science, vol. 157, pp. 110–117.
D. Sinha and M. El-Sharkawy, “Thin MobileNet: An Enhanced MobileNet Architecture,” 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York, NY, USA, pp. 0280-0285, 2019.
F. Zhu, C. Liu, J. Yang and S. Wang, “An Improved MobileNet Network with Wavelet Energy and Global Average Pooling for Rotating Machinery Fault Diagnosis,” Sensors, vol. 22, no. 12, p. 4427, 2022.
V. C R, V. Asha, U. M and T. M, “Glaucoma Detection using Transfer Learning,” 2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS), Coimbatore, India, pp. 476-481, 2023.
R. Patil and S. Sharma, “Automatic glaucoma detection from fundus images using transfer learning,” Multimedia Tools and Applications, Springer, 2024.
https://www.kaggle.com/datasets/lokeshsaipureddi/drishtigs-retina-dataset-for-onh-segmentation
https://www.kaggle.com/datasets/deathtrooper/eyepacs-airogs-light
https://ieee-dataport.org/documents/refuge-retinal-fundus-glaucoma-challenge
https://github.com/cvblab/retina_dataset
https://data.mendeley.com/datasets/trghs22fpg/4
https://www.kaggle.com/datasets/arnavjain1/glaucoma-datasets
https://figshare.com/articles/dataset/PAPILA/14798004
M. Tariq, V. Palade and Y. Ma, “Transfer learning-based classification of Diabetic Retinopathy on the Kaggle EyePACS dataset,” Medical Imaging and Computer-Aided Diagnosis, vol. 810, pp.89-99, 2023.
S. Das, M. Mishra, and S. Majumder, “A Comprehensive Analysis of Diabetic Retinopathy Detection in Retinal Fundus Images Using Different Convolutional Neural Network,” ECTI-CIT Transactions, vol. 17, no. 4, pp. 510–521, Nov. 2023.
A. Shoukat, S. Akbar, S. A. Hassan, S. Iqbal, A. Mehmood and Q. M. Ilyas, “Automatic Diagnosis of Glaucoma from Retinal Images Using Deep Learning Approach,” Diagnostics, vol. 13, no. 10, pp. 1738–173, 2023.
N. Mojab, V. Noroozi, P. S. Yu and J. A. Hallak, “Deep Multi-Task Learning for Interpretable Glaucoma Detection,” 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI), Los Angeles, CA, USA, pp. 167-174, 2019.
R. Blagus and L. Lusa, “SMOTE for highdimensional class-imbalanced data,” BMC Bioinformatics, vol. 14, no. 106, 2013.
Y. Chai, H Liu and J. Xu, “Glaucoma diagnosis based on both hidden features and domain knowledge through deep learning models,” Knowledge-Based Systems, vol. 161, pp. 147-156, 2018.
L. Brigatti, D Hoffman and J Caprioli, “Neural networks to identify Glaucoma with structural and functional measurements,” Am. J. Ophthalmol, vol. 121, no. 5, pp. 511–521, 1996.
E. Deepika and S. Maheswari, “Earlier glaucoma detection using blood vessel segmentation and classification,” 2018 2nd International Conference on Inventive Systems and Control (ICISC), Coimbatore, India, pp. 484-490, 2018.
H. Kang, X. Li and X. Su, “Cup-disc and retinal nerve fiber layer features fusion for diagnosis glaucoma,” Medical Imaging 2020: ComputerAided Diagnosis, vol. 11314, no. 113143z, 2020.
D. Parashar and D. K. Agrawal, “Automated Classification of Glaucoma Stages Using Flexible Analytic Wavelet Transform From Retinal Fundus Images,” in IEEE Sensors Journal, vol. 20, no. 21, pp. 12885-12894, 1 Nov.1, 2020.
O. J. Afolabi, G. P. Mabuza-Hocquet, F. V. Nelwamondo and B. S. Paul, “The Use of U-Net Lite and Extreme Gradient Boost (XGB) for Glaucoma Detection,” in IEEE Access, vol. 9, pp.47411-47424, 2021.
Y. Xu, M. Hu, H. Liu, H. Yang, H. Wang, S. Lu, T. Liang, X. Li, M. Xu, L. Li, H. Li, X. Ji, Z. Wang, L. Li, R. N. Weinreb and N. Wang, “A hierarchical deep learning approach with transparency and interpretability based on small samples for glaucoma diagnosis,” NPJ digital medicine, vol. 4, no. 48, pp. 1-11, 2021.
S. Lenka and Z. L. Mayaluri, “Hybrid glaucoma detection model based on reflection components separation from retinal fundus images,” EAI Endorsed Transactions Research Article on Pervasive Health and Technology, vol. 9, pp. 1-13, 2023.