A contrastive study of analyzing the proficiency of different neural networks for ocular diagnosis
About this article
Abstract
Nowadays, eye diseases are becoming common and diagnosing them quickly and instantly can save the patient'sd eyes otherwise it can lead to permanent blindness. This study compares the performance of five new modern advanced neural networks that are not widely used - ConvNeXt, Swin Transformer, CoAtNet, LeViT, and EfficientFormer in detecting patient eye disease. By comparing these models with each other, we aim to find the most effective and accurate model for detecting eye diseases. This comprehensive study undertakes an exhaustive examination of various machine learning models trained on an eye disease dataset. Through a meticulous comparative analysis, we assessed these models' relative efficacies and accuracies. Our investigation aims to elucidate which architectural design performs optimally in classifying ocular pathologies, thereby contributing to the advancement of more precise and expeditious diagnostic modalities for eye disorders. Our research endeavors to identify the most effective neural network configuration for automated eye disease classification. By conducting this in-depth comparative study, we aspire to provide valuable insights into the field of medical image analysis. Our findings hold the potential to inform the development of more accurate, efficient, and reliable diagnostic tools in ophthalmology. Ultimately, this study seeks to enhance the quality of patient care by facilitating faster and more precise diagnoses, as well as promoting early detection of ocular diseases. Our research contributes to the growing body of literature on artificial intelligence applications in medical diagnostics. By systematically comparing various architectures, we provide a nuanced understanding of their relative merits in addressing complex visual recognition tasks in ophthalmology. This study serves as a foundation for future investigations aimed at optimizing AI-driven diagnostic tools for improved patient outcomes in eye care.
References
R. Pahuja, U. Sisodia, A. Tiwari, S. Sharma, and P. Nagrath, “A dynamic approach of eye disease classification using deep learning and machine learning model,” in Proceedings of Data Analytics and Management, Singapore: Springer Nature Singapore, 2022, pp. 719–736. https://link.springer.com/chapter/10.1007/978-981-16-6289-8_59?. https://doi.org/10.1007/978-981-16-6289-8_59.
C. Y. Cheung, F. Tang, D. S. W. Ting, G. S. W. Tan, and T. Y. Wong, “Artificial intelligence in diabetic eye disease screening,” Asia Pac. J. Oph-thalmol. (Phila.), 2019. https://eandv.biomedcentral.com/articles/10.1186/s40662-024-00389-y.
P. Chakraborty and C. Tharini, “Pneumonia and eye disease detection using convolutional neural networks. Engineering,” Technology & Applied Science Research, vol. 10, no. 3, pp. 5769–5774, 2020. https://shmpublisher.com/index.php/joscex/article/view/493. https://doi.org/10.48084/etasr.3503.
R. Sarki, K. Ahmed, H. Wang, Y. Zhang, and K. Wang, “Convolutional neural network for multi-class classification of diabetic eye disease,” ICST Trans. Scalable Inf. Syst., p. 172436, 2018 https://eudl.eu/doi/10.4108/eai.16-12-2021.172436. https://doi.org/10.4108/eai.16-12-2021.172436.
P. Glaret Subin and P. Muthukannan, “Optimized convolution neural network based multiple eye disease detection,” Comput. Biol. Med., vol. 146, no. 105648, p. 105648, 2022. https://pubmed.ncbi.nlm.nih.gov/35751184/. https://doi.org/10.1016/j.compbiomed.2022.105648.
View more references (10)
N. Gour and P. Khanna, “Multi-class multi-label ophthalmological disease detection using transfer learning based convolutional neural network,” Biomed. Signal Process. Control, vol. 66, no. 102329, p. 102329, 2021. https://www.sciencedirect.com/science/article/abs/pii/S1746809420304432. https://doi.org/10.1016/j.bspc.2020.102329.
S. Shamas, S. N. Panda, and I. Sharma, “Review on lung nodule segmentation-based lung cancer classification using machine learning approaches,” in Artificial Intelligence on Medical Data, Singapore: Springer Nature Singapore, 2023,pp. 277–286. https://sirsyeduniversity.edu.pk/ssurj/rj/index.php/ssurj/article/view/643. https://doi.org/10.1007/978-981-19-0151-5_24.
Ramneet, D. Gupta, and M. Madhukar, “Analysis of machine learning approaches for sentiment analysis of Twitter data,” J. Comput. Theor. Na-nosci., vol. 17, no. 9, pp. 4535–4542, 2020. https://link.springer.com/chapter/10.1007/978-981-99-6620-2_9. https://doi.org/10.1166/jctn.2020.9300.
R. Sharma, V. Kukreja and V. Kadyan, "Hispa Rice Disease Classification using Convolutional Neural Network," 2021 3rd International Confer-ence on Signal Processing and Communication (ICPSC), Coimbatore, India, 2021, pp. 377-381. https://www.semanticscholar.org/paper/Hispa-Rice-Disease-Classification-using-Neural-Sharma-Kukreja/8761632fb489e99863091d656be5324c0227614b. https://doi.org/10.1109/ICSPC51351.2021.9451800.
S. Sharma and K. Guleria, “A Deep Learning based model for the Detection of Pneumonia from Chest X-Ray Images using VGG-16 and Neural Networks,” Procedia Comput. Sci., vol. 218, pp. 357–366, 2023.https://www.sciencedirect.com/science/article/pii/S1877050923000182. https://doi.org/10.1016/j.procs.2023.01.018.
S. Kumar, S. Pathak, and B. Kumar, “Automated detection of eye-related diseases using digital image processing,” in Handbook of multimedia information security: techniques and applications, 2019, pp. 513–544. https://link.springer.com/chapter/10.1007/978-3-030-15887-3_25. https://doi.org/10.1007/978-3-030-15887-3_25.
R. Sarki, K. Ahmed, H. Wang, Y. Zhang, J. Ma, and K.Wang, “Image preprocessing in classification and identification of diabetic eye diseases,” Data Sci. Eng., vol. 6, no. 4, pp. 455–471, 2021. https://pmc.ncbi.nlm.nih.gov/articles/PMC8370665/. https://doi.org/10.1007/s41019-021-00167-z.
T. Nazir, A. Irtaza, A. Javed, H. Malik, D. Hussain, and R. A. Naqvi, “Retinal image analysis for diabetes-based eye disease detection using deep learning,” Appl. Sci. (Basel), vol. 10, no. 18, p. 6185, 2020.https://www.mdpi.com/2076-3417/10/18/6185. https://doi.org/10.3390/app10186185.
L. Jain, H. V. S. Murthy, C. Patel, and D. Bansal, “Retinal eye disease detection using deep learning,” in 2018 Fourteenth International Conference on Information Processing (ICINPRO), 2018. https://www.semanticscholar.org/paper/Retinal-Eye-Disease-Detection-Using-Deep-Learning-Jain-Murthy/626f6e8d031d96d4aaadacd60e991a746d6ab1a6. https://doi.org/10.1109/ICINPRO43533.2018.9096838.
P. Muthukannan, “Optimized convolution neural network-based multiple eye disease detection,” Computers in Biology and Medicine, vol. 146, 2022. https://www.sciencedirect.com/science/article/abs/pii/S0010482522004401. https://doi.org/10.1016/j.compbiomed.2022.105648.