پیوندهای مفید
آمار
| تعداد نشریات | 8 |
| تعداد شمارهها | 435 |
| تعداد مقالات | 5,634 |
| تعداد مشاهده مقاله | 7,427,857 |
| تعداد دریافت فایل اصل مقاله | 6,215,930 |
Diabetic Retinopathy Detection from Retinal Images Using the Pyramid Vision Transformer Method | ||
| AUT Journal of Electrical Engineering | ||
| دوره 57، Issue 2 (Special Issue)، 2025، صفحه 399-408 اصل مقاله (866 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/eej.2024.23500.5621 | ||
| نویسندگان | ||
| Samaneh Dehghani؛ Hossein Ebrahimnezhad* ؛ Nasrin Rahmani | ||
| Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran | ||
| چکیده | ||
| The development of automated diagnostic tools is essential for efficiently analyzing medical data, particularly for conditions like Diabetic Retinopathy, a leading cause of vision loss and blindness in adults. The Asia Pacific Tele-Ophthalmology Society 2019 blindness detection dataset, which includes detailed retinal images, is crucial for the advancement of these tools. This study focuses on using the Pyramid Vision Transformer to improve both the accuracy and efficiency of Diabetic Retinopathy detection. Unlike the traditional Vision Transformer, which is computationally expensive and produces low-resolution outputs due to its single-scale structure, Pyramid’s multi-scale architecture enables more efficient feature representation. This design allows for better management of large feature maps and enhances image resolution, both vital for precise diagnoses. By implementing the Pyramid Vision Transformer, our approach not only increases accuracy but also improves resource efficiency, outperforming conventional Convolutional Neural Networks. Extensive experiments demonstrate that the model significantly boosts detection and classification accuracy, making it a valuable tool for clinical applications. The model achieved 92.38% accuracy and an Area Under the Curve of 99.58%. These results highlight the model's effectiveness in real-world applications. Future research will focus on optimizing the model for even better performance and exploring its clinical integration to further enhance the diagnostic process in healthcare. | ||
| کلیدواژهها | ||
| Diabetic Retinopathy؛ Pyramidal Vision Transformer؛ Detection؛ Blindness؛ Retinal Images | ||
| مراجع | ||
|
[1] S. Sunkari, A. Sangam, M. Suchetha, R. Raman, R. Rajalakshmi, S. Tamilselvi, A refined ResNet18 architecture with Swish activation function for Diabetic Retinopathy classification, Biomedical Signal Processing and Control, 88 (2024) 105630.
[2] A. Biran, Automatic detection and classification of diabetic retinopathy from retinal fundus images, Toronto Metropolitan University.
[3] K. Shankar, A.R.W. Sait, D. Gupta, S.K. Lakshmanaprabu, A. Khanna, H.M. Pandey, Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model, Pattern Recognition Letters, 133 (2020) 210-216.
[4] M. Canayaz, Classification of diabetic retinopathy with feature selection over deep features using nature-inspired wrapper methods, Applied Soft Computing, 128 (2022) 109462.
[5] V.P.C. Reddy, K.K. Gurrala, OHGCNet: optimal feature selection-based hybrid graph convolutional network model for joint DR-DME classification, Biomedical Signal Processing and Control, 78 (2022) 103952.
[6] S.Z. Beevi, Multi-Level severity classification for diabetic retinopathy based on hybrid optimization enabled deep learning, Biomedical Signal Processing and Control, 84 (2023) 104736.
[7] R.C. Joshi, A.K. Sharma, M.K. Dutta, VisionDeep-AI: Deep learning-based retinal blood vessels segmentation and multi-class classification framework for eye diagnosis, Biomedical Signal Processing and Control, 94 (2024) 106273.
[8] G.T. Zago, R.V. Andreão, B. Dorizzi, E.O.T. Salles, Diabetic retinopathy detection using red lesion localization and convolutional neural networks, Computers in biology and medicine, 116 (2020) 103537.
[9] F.J.M. Shamrat, R. Shakil, B. Akter, M.Z. Ahmed, K. Ahmed, F.M. Bui, M.A. Moni, An advanced deep neural network for fundus image analysis and enhancing diabetic retinopathy detection, Healthcare Analytics, 5 (2024) 100303.
[10] M. Phridviraj, R. Bhukya, S. Madugula, A. Manjula, S. Vodithala, M.S. Waseem, A bi-directional Long Short-Term Memory-based Diabetic Retinopathy detection model using retinal fundus images, Healthcare Analytics, 3 (2023) 100174.
[11] D.A. Da Rocha, F.M.F. Ferreira, Z.M.A. Peixoto, Diabetic retinopathy classification using VGG16 neural network, Research on Biomedical Engineering, 38(2) (2022) 761-772.
[12] W. Wang, E. Xie, X. Li, D.-P. Fan, K. Song, D. Liang, T. Lu, P. Luo, L. Shao, Pyramid vision transformer: A versatile backbone for dense prediction without convolutions, in: Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 568-578.
[13] M. Karthik, S. Dane, Aptos 2019 blindness detection, Kaggle https://kaggle. com/competitions/aptos2019-blindness-detection Go to reference in, (2019) 5.
[14] M. Xu, S. Yoon, A. Fuentes, D.S. Park, A comprehensive survey of image augmentation techniques for deep learning, Pattern Recognition, 137 (2023) 109347.
[15] S. Wu, P. Flach, A scored AUC metric for classifier evaluation and selection, in: Second workshop on ROC analysis in ML, bonn, Germany, Citeseer, 2005.
[16] A. Gunawardana, G. Shani, A survey of accuracy evaluation metrics of recommendation tasks, Journal of Machine Learning Research, 10(12) (2009).
[17] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, An image is worth 16x16 words: Transformers for image recognition at scale, arXiv preprint arXiv:2010.11929, (2020). | ||
|
آمار تعداد مشاهده مقاله: 678 تعداد دریافت فایل اصل مقاله: 455 |
||