A Review on the Detection and Classification of Glaucoma Disease Based on Transfer Learning
Keywords:
Glaucoma, Transfer learning, ResNet50, EffientNet, CNN.Abstract
An eye infection is a condition affecting the eyes that can be caused by a bacterium, virus, or fungus. Numerous eye infections exist, such as glaucoma, cellulitis, keratitis, and conjunctivitis. A few of the symptoms may be itching, discharge, altered eyesight and others. Antibiotics are not effective in treating viral infections. Antibiotics treat infections caused by bacteria exclusively. A class of eye infection known as glaucoma can result in blindness and visual loss by harming the optic nerve, a nerve located at the back of the eye. You might not notice the symptoms at first because they can appear so slowly. A thorough dilated eye exam is the only way to determine if you have glaucoma . Efforts have been done to automate the procedures for the recognition and classification of glaucoma. In this paper, we have proposed a transfer learning model by reviewing pre-trained models and the model is able to provide a better accuracy. Our model is classifying the datasets into positive and negative cases during testing and validation. We utilize different prêt rained models, that are ResNet50 (90%), EffiecintNet (78%) and CNN(79%) evaluate how well they perform when trained using various optimizers. Our results show differences in accuracy and provide important information about the possibility of these models for the detection of glaucoma. An important first step towards improving the precision and dependability of glaucoma detection models in clinical settings is represented by this work.
References
S. Serte and A. Serener, "A generalized deep learning model for glaucoma detection," in 2019 3rd International symposium on multidisciplinary studies and innovative technologies (ISMSIT), Oct. 2019, pp. 1-5.
Ana-Maria ?tefan, Elena Paraschiv, Silvia Ovreiu, and Elena Ovreiu, "A Review of Glaucoma Detection from Digital Fundus Images using Machine Learning Techniques," 2020. DOI: 10.1109/EHB50910.2020.9280218.
Tehmina Khalil et al, "Review of Machine Learning Techniques for Glaucoma Detection and Prediction," in Science and Information Conference, Aug. 2014, London, UK.
M. Wang et al., "Deep learning model for multi-classification of infectious diseases from unstructured electronic medical records," BMC Med Inform Decis Mak, vol. 22, p. 41, 2022. DOI: 10.1186/s12911-022-01776.
Ali Serener and Sertan Serte, "Transfer Learning for Early and Advanced Glaucoma Detection with Convolutional Neural Networks," in 2019 Medical Technologies Congress (TIPTEKNO), 2019, pp. 1-4.
M. Eswari and Saminathan Balamurali, "An Intelligent Machine Learning Support System for Glaucoma Prediction Among Diabetic Patients," 2021, pp. 447-449. DOI: 10.1109/ICACITE51222.2021.9404635.
S. Yousef et al., "Glaucoma Progression Detection Using Structural Retinal Nerve Fiber Layer Measurements and Functional Visual Field Points," IEEE Transactions On Biomedical Engineering, vol. 61, no. 4, Apr. 2014.
V. Thangaraj and V. Natarajan, "Glaucoma diagnosis using support vector machine," in 2017 International Conference on Intelligent Computing and Control Systems (ICICCS), Jun. 2017, pp. 394-399.
A. Pandey, P. Patre, and J. Minj, "Detection of glaucoma disease using image processing, soft computing and deep learning approaches," in 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Oct. 2020, pp. 1-7.
S. H. Lu et al., "Comparison of Ocular Biomechanical Machine Learning Classifiers for Glaucoma Diagnosis," in 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Dec. 2018, pp. 2539-2543.
S. Ovreiu, I. Cristescu, F. Balta, A. Sultana, and E. Ovreiu, "Early detection of glaucoma using residual networks," in 2020 13th International Conference on Communications (COMM), Jun. 2020, pp. 161-164.
X. Chen, Y. Xu, D. W. K. Wong, T. Y. Wong, and J. Liu, "Glaucoma detection based on deep convolutional neural network," in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Aug. 2015, pp. 715-718.
J. Civit-Masot, M. J. Domínguez-Morales, S. Vicente-Díaz, and A. Civit, "Dual machine-learning system to aid glaucoma diagnosis using disc and cup feature extraction," IEEE Access, vol. 8, pp. 127519-127529, 2020.
A. Sallam et al., "Early detection of glaucoma using transfer learning from pre-trained CNN models," in 2021 International Conference of Technology, Science and Administration (ICTSA), Mar. 2021, pp. 1-5.
T. I. Chayan, A. Islam, E. Rahman, M. T. Reza, T. S. Apon, and M. G. R. Alam, "Explainable AI based glaucoma detection using transfer learning and LIME," in 2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), Dec. 2022, pp. 1-6.
A. Diaz-Pinto, S. Morales, V. Naranjo, T. Köhler, J. M. Mossi, and A. Navea, "CNNs for automatic glaucoma assessment using fundus images: an extensive validation," Biomedical Engineering Online, vol. 18, pp. 1-19, 2019.
J. J. Gómez-Valverde et al., "Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning," Biomedical Optics Express, vol. 10, no. 2, pp. 892-913, 2019.
A.-M. ?tefan, E. Paraschiv, S. Ovreiu, and E. Ovreiu, "A Review of Glaucoma Detection from Digital Fundus Images using Machine Learning Techniques," 2020. DOI: 10.1109/EHB50910.2020.9280218.
A. Saxena, A. Vyas, L. Parashar, and U. Singh, "A Glaucoma Detection using Convolutional Neural Network," pp. 815-820, 2020. DOI: 10.1109/ICESC48915.2020.9155930.
