Optimized Screening of Glaucoma using Fundus Images and Deep Learning
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Abstract
Diabetic retinopathy, glaucoma, and age-related macular degeneration are among the leading causes of global visual loss. Early detection and diagnosis of these conditions are crucial to reduce vision loss and improve patient outcomes. In recent years, deep learning algorithms have shown great potential in automating the diagnosis and categorization of eye disorders using medical photos. For this purpose, the ResNet-50 architecture is employed in a deep learning-based strategy. The approach involves fine-tuning a pre-trained ResNet-50 model using over 5,000 retinal pictures from the ODIR dataset, covering ten different ocular diseases. To enhance the model's generalization performance and avoid overfitting, various data augmentation techniques are applied to the training data. The model successfully detects glaucoma-related ocular illnesses, including cataract, diabetic retinopathy, and healthy eyes. Performance evaluation using metrics like accuracy, precision, recall, and F1-score shows that the model achieved 92.60% accuracy, 93.54% precision, 91.60% recall, and an F1-score of 91.68%. These results indicate that the proposed strategy outperforms many state-of-the-art approaches in the detection and categorization of eye disorders. This success underscores the potential of deep learning-based methods in automated ocular illness identification, facilitating early diagnosis and timely treatment to ultimately improve patient outcomes.
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References
Li, Xiaomeng & Jia, Mengyu & Islam, Md Tauhidul & Yu, Lequan & Xing, Lei. (2020). “Self-Supervised Feature Learning via Exploiting Multi-Modal Data for Retinal Disease Diagnosis”. IEEE Transactions on Medical Imaging. PP. 1-1. 10.1109/TMI.2020.3008871.
G. Ramanathan, D. Chakrabarti, A. Patil, S. Rishipathak and S. Kharche, "Eye Disease Detection Using Machine Learning," 2021 2nd Global Conference for Advancement in Technology (GCAT), 2021, pp. 1-5, doi: 10.1109/GCAT52182.2021.9587740.
D. Selvathi and K. Suganya, "Support Vector Machine Based Method for Automatic Detection of Diabetic Eye Disease using Thermal Images," 2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT), 2019, pp. 1-6, doi: 10.1109/ICIICT1.2019.8741450.
C. Li, J. Ye, J. He, S. Wang, Y. Qiao and L. Gu, "Dense Correlation Network for Automated Multi-Label Ocular Disease Detection with Paired Color Fundus Photographs," 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 2020, pp. 1-4, doi: 10.1109/ISBI45749.2020.9098340.
Malliga Subramanian, M. Sandeep Kumar, V. E. Sathishkumar, Jayagopal Prabhu, Alagar Karthick, S. Sankar Ganesh, Mahseena Akter Meem, "Diagnosis of Retinal Diseases Based on Bayesian Optimization Deep Learning Network Using Optical Coherence Tomography Images", Computational Intelligence and Neuroscience, vol. 2022, Article ID 8014979, 15 pages, 2022. https://doi.org/10.1155/2022/8014979
Yi-Peng Liu, Zhanqing Li, Cong Xu, Jing Li, Ronghua Liang,Referable diabetic retinopathy identification from eye fundus images with weighted path for convolutional neural network, Artificial Intelligence in Medicine, Volume 99,2019,101694,ISSN 0933-3657
L. Jain, H. V. S. Murthy, C. Patel and D. Bansal, "Retinal Eye Disease Detection Using Deep Learning," 2018 Fourteenth International Conference on Information Processing (ICINPRO), 2018, pp. 1-6, doi: 10.1109/ICINPRO43533.2018.9096838.
H. Jiang, K. Yang, M. Gao, D. Zhang, H. Ma and W. Qian, "An Interpretable Ensemble Deep Learning Model for Diabetic Retinopathy Disease Classification," 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019, pp. 2045-2048, doi: 10.1109/EMBC.2019.8857160.
K. Vayadande, V. Ingale, V. Verma, A. Yeole, S. Zawar and Z. Jamadar, "Ocular Disease Recognition using Deep Learning," 2022 International Conference on Signal and Information Processing (IConSIP), Pune, India, 2022, pp. 1-7, doi: 10.1109/ICoNSIP49665.2022.10007470.
N. Li, T. Li, C. Hu, K. Wang and H Kang, "A Benchmark of Ocular Disease Intelligent Recognition: One Shot for Multi-disease Detection" in Benchmarking Measuring and Optimizing. Bench 2020. Lecture Notes in Computer Science, Springer, Cham, vol. 12614, 2021.
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), Coimbatore, India, 2021, pp. 1-5, doi: 10.1109/ICCCI50826.2021.9402566.
A. Pandey, P. Patre and J. Minj, "Detection of Glaucoma Disease using Image Processing, Soft Computing and Deep Learning Approaches," 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I- SMAC), Palladam, India, 2020, pp. 1-7, doi: 10.1109/I-SMAC49090.2020.9243596.
Simi Sanya, M Seetha, 2021, Detection of Ocular Diseases using Ensemble of Deep Learning Models, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 10, Issue 09 (September 2021)
Agustino Halim, Sani Muhamad Isa. (2023). Electrocardiogram Signal Classification for Diagnosis Sudden Cardiac Death Using 2D CNN and LSTM. International Journal of Intelligent Systems and Applications in Engineering, 11(4s), 558–564. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2726
Anand Deshpande, Vania V. Estrela, Prashant Patavardhan, "The DCT-CNN-ResNet50 architecture to classify brain tumors with super-resolution, convolutional neural network", and the ResNet50, Neuroscience Informatics, Volume 1, Issue 4, 2021, 100013, ISSN 2772-5286, https://doi.org/10.1016/j.neuri.2021.100013.
Lam C, Yi D, Guo M, Lindsey T. Automated Detection of Diabetic Retinopathy using Deep Learning. AMIA Jt Summits Transl Sci Proc. 2018 May 18; 2017:147-155. PMID: 29888061; PMCID: PMC5961805.
S. K. A. S, S. S, A. S. M and S. K. S, "Machine Learning based Ideal Job Role Fit and Career Recommendation System," 2023 7th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2023, pp. 64-67, doi: 10.1109/ICCMC56507.2023.10084315.
A. S. M, S. S and S. K. S, "A Study on various Applications of Computer Vision for Teaching Learning in Classroom," 2022 6th International Conference on Electronics, Communication and Aerospace Technology, Coimbatore, India, 2022, pp. 896-900, doi: 10.1109/ICECA55336.2022.10009136.
C. Esteves, C. Allen-Blanchette, X. Zhou, and K. Daniilidis, “Polar Transformer Networks,” in ICLR, 2018, pp. 1–14.
M. Rezaei, F. Soleymani, B. Bischl and S. Azizi “Deep Bregman Divergence for Contrastive Learning of Visual Representations”, arXiv Computer Science, Vol.2, 2021, pp.1-11, doi:10.48550/arXiv.2109.07455.
Santhosh, S., and D. Veerabhadra Babu. "DEVELOPING A HYBRID APPROACH FOR ENHANCEDEARLY ACUTE GLAUCOMA SCREENING BY COMBINED DEEP LEARNING APPROACH."
H. N.Veena, A. Muruganandham and T. S. Kumaran, “A novel optic disc and optic cup segmentation technique to diagnose glaucoma using deep learning convolutional neural network over retinal fundus images”, Elsevier, Journal of King Saud University - Computer and Information Sciences, Vol. 34, Issue 8, 2022, pp. 6187-6198, doi:10.1016/j.jksuci.2021.02.003.
P. Tanna, R. W Strauss, K. Fujinami, and M. Michaelides, “Stargardt disease: clinical features, molecular genetics, animal models and therapeutic options”, The British Journal of Ophthalmology, Vol. 101, 2017, pp. 25-30 doi: 10.1136/bjophthalmol-2016-308823.
J. H. Tan, U. R. Acharya, S. V. Bhandary, K. C. Chua and S. Sivaprasad, “Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network”, Journal of Computational Science, Vol. 20, 2017, pp.70-79, doi:10.1016/j.jocs.2017.02.006.
ACRIMA-database, https://figshare.com/articles/CNNs_for_Automatic_Glaucoma_Assessment_using_Fundus_Images_An_Extensive_Validation/7613135, 2019
Retina-image-bank-database, https://imagebank.asrs.org/file/28619/stargardt-disease, 2018
Anthony Thompson, Ian Martin, Alejandro Perez, Luis Rodriguez, Diego Rodríguez. Utilizing Machine Learning for Educational Game Design. Kuwait Journal of Machine Learning, 2(2). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/183
DIARETDB0 – Standard Diabetic Retinopathy Database, https://www.it.lut.fi/project/imageret/diaretdb0/, 2007
U. Raghavendra, A. Gudigar, S. V. Bhandary, T. N. Rao, E. J. Ciaccio and U. R. Acharya, “A Two Layer Sparse Autoencoder for Glaucoma Identification with Fundus Images”, Image & Signal Processing, Vol.43, 2019, pp. 1-9, doi: 10.1007/s10916-019-1427-x.
J. Martins, J. S. Cardoso and F. Soares, “Offline computer-aided diagnosis for Glaucoma detection using fundus images targeted at mobile devices”, Computer Methods and Programs in Biomedicine, Vol. 192, 2020, pp.1-14, doi: 10.1016/j.cmpb.2020.105341.
Juhani Nieminen , Johan Bakker, Martin Mayer, Patrick Schmid, Andrea Ricci. Exploring Explainable AI in Educational Machine Learning Models. Kuwait Journal of Machine Learning, 2(2). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/191
C. Bowd, A. Belghith, J. A. Proudfoot, L. M. Zangwill, M. Christopher, M. H. Goldbaum, H. Hou, R. C. Penteado, S. Moghimi and R. N. Weinreb, “Gradient-Boosting Classifiers Combining Vessel Density and Tissue Thickness Measurements for Classifying Early to Moderate Glaucoma”, American Journal of Ophthalmology, Vol. 217, 2020, pp.131-139, doi:10.1016/j.ajo.2020.03.024.
S. S and D. V. Babu, "Retinal Glaucoma Detection from Digital Fundus Images using Deep Learning Approach," 2023 7th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2023, pp. 68-72, doi: 10.1109/ICCMC56507.2023.10083712.