Comparative Analysis Techniques for Early Detection and Staging of Diabetic Retinopathy
Keywords:
DR Detection, Deep Learning Models, Convolutional neural networks (CNNs), DR Grading, Retinal fundus imagesAbstract
The escalating global prevalence of diabetes underscores the urgent need for effective early detection and grading methods to mitigate complications such as Diabetic Retinopathy (DR), which can lead to significant vision impairment. This paper provides a comprehensive review of Deep Learning (DL) based techniques for diagnosing and grading DR from retinal fundus images, emphasizing the importance of accurate and timely diagnosis and classification. This research evaluates various DL models, with a particular focus on convolutional neural networks (CNNs), for detecting and grading DR. The study examines common datasets to assess the performance of these models. By analyzing the efficacy of different DL architectures across diverse datasets, the study aims to highlight their strengths and weaknesses in handling the complexities of both DR detection and severity grading. The findings highlight significant advancements in AI-driven DR detection and grading. The review shows that while some DL models excel in specific aspects, no single model consistently outperforms others across all metrics. There is a promising trend towards improving diagnostic accuracy and grading consistency, demonstrating the potential of these technologies for early diagnosis and DR severity classification. This study emphasizes the need for ongoing development of DL-based methods for DR detection and grading. It highlights AI's potential to improve early and accurate diagnosis, crucial for effective treatment and better patient outcomes. The insights from this review support efforts to create more reliable AI diagnostic tools for DR, addressing a critical need in the global diabetes epidemic.
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S. Aparimita, M. S. N. Mohanty, and A. C. Das, "Comparative risk analysis on prediction of diabetes mellitus using machine learning approach," IEEE, pp. 3312–3317, 2016.
IDF Diabetes Atlas 9th Edition, 2022. Available online: https://diabetesatlas.org/atlas/ninth-edition/.
O. Erkaymaz and M. Ozer, "Impact of small-world network topology on the conventional artificial neural network for the diagnosis of diabetes," Sci. Direct, vol. 8, 2016.
P. Nijalingappa and B. Sandeep, "Machine learning approach for the identification of diabetes retinopathy and its stages," in Proc. 2015 Int. Conf. Applied Theoretical Computing and Communication Technology (iCATccT), Davangere, Karnataka, India, pp. 653–658, 2015.
S. Joshi and M. Borse, "Detection and prediction of diabetes mellitus using back-propagation neural network," IEEE, vol. 4, 2016.
C. Raja and L. Balaji, "An automatic detection of blood vessel in retinal images using convolution neural network for diabetic retinopathy detection," Pattern Recognit. Image Anal., vol. 29, pp. 533–545, 2019.
T. Nazir, A. Irtaza, Z. Shabbir, A. Javed, U. Akram, and M. T. Mahmood, "Diabetic retinopathy detection through novel tetragonal local octa patterns and extreme learning machines," Artif. Intell. Med., vol. 99, p. 101695, 2019.
L. S. Chen and S. J. Cai, "Neural-network-based resampling method for detecting diabetes mellitus," J. Med. Biol. Eng., vol. 35, pp. 824–832, 2015. doi: 10.1007/s40846-015-0093-9.
N. Nasir, P. Oswald, O. Alshaltone, F. Barneih, M. Al Shabi, and A. Al-Shammaa, "Deep DR: Detection of diabetic retinopathy using a convolutional neural network," in Proc. 2022 Advances in Science and Engineering Technology Int. Conf. (ASET), Dubai, UAE, pp. 1–5, Feb. 2022.
N. Chakrabarty, "A deep learning method for the detection of diabetic retinopathy," in Proc. 2018 5th IEEE Uttar Pradesh Section Int. Conf. Electrical, Electronics and Computer Engineering (UPCON), Gorakhpur, India, pp. 1–5, Nov. 2018.
S. Rêgo, M. Dutra-Medeiros, F. Soares, and M. Monteiro-Soares, "Screening for diabetic retinopathy using an automated diagnostic system based on deep learning: Diagnostic accuracy assessment," Ophthalmologica, vol. 244, pp. 250–257, 2021.
M. Kolla and T. Venugopal, "Efficient classification of diabetic retinopathy using binary CNN," in Proc. 2021 Int. Conf. Computational Intelligence and Knowledge Economy (ICCIKE), Dubai, UAE, pp. 244–247, Mar. 2021.
T. Zheng et al., "A machine learning-based framework to identify type 2 diabetes through electronic health records," Sci. Direct, vol. 97, pp. 120–127, Jan. 2017.
A. M. Pamadi, A. Ravishankar, P. A. Nithya, G. Jahnavi, and S. Kathavate, "Diabetic retinopathy detection using MobileNetV2 architecture," in Proc. 2022 Int. Conf. Smart Technologies and Systems for Next Generation Computing (ICSTSN), Villupuram, India, pp. 1–5, Mar. 2022.
R. S. Mangrulkar, "Retinal image classification technique for diabetes identification," in 2017 Int. Conf. Comput. Methodol. Commun. ICCMC, Erode, IEEE, pp. 961–966, 2017.
P. Saranya, S. K. Devi, and B. Bharanidharan, "Detection of diabetic retinopathy in retinal fundus images using DenseNet based deep learning model," in Proc. 2022 Int. Mobile and Embedded Technology Conf. (MECON), Noida, India, pp. 268–272, Mar. 2022.
S. Sanjana, N. S. Shadin, and M. Farzana, "Automated diabetic retinopathy detection using transfer learning models," in Proc. 2021 5th Int. Conf. Electrical Engineering and Information & Communication Technology (ICEEICT), Mirpur, Dhaka, pp. 1–6, Nov. 2021.
N. S. Kumar and B. R. Karthikeyan, "Diabetic retinopathy detection using CNN, transformer and MLP based architectures," in Proc. 2021 Int. Symp. Intelligent Signal Processing and Communication Systems (ISPACS), Hualien City, Taiwan, pp. 1–2, Nov. 2021.
C. Lahmar and A. Idri, "Deep hybrid architectures for diabetic retinopathy classification," in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, Taylor & Francis: Oxfordshire, UK, pp. 1–19, 2022.
S. A. Dwivedi, Y. A. Kalin, G. K. Roy, and K. Singla, "Real-time detection for normal, glaucomatous and diabetic retinopathy eyes for ophthalmoscopy using deep learning," in Proc. 2022 9th Int. Conf. Computing for Sustainable Global Development (INDIACom), New Delhi, India, pp. 598–603, Mar. 2022.
N. P. Kazakh-British, A. Pak, and D. Abdullina, "Automatic detection of blood vessels and classification in retinal images for diabetic retinopathy diagnosis with application of convolution neural network," in Proc. 2018 Int. Conf. Sensors, Signal and Image Processing, Prague, Czech Republic, pp. 60–63, Oct. 2018.
K. Gunasekaran et al., "A deep learning framework for earlier prediction of diabetic retinopathy from fundus photographs," BioMed Res. Int., vol. 2022, p. 3163496, 2022.
A. Umapathy, A. Sreenivasan, D. S. Nairy, S. Natarajan, and B. N. Rao, "Image processing, textural feature extraction and transfer learning based detection of diabetic retinopathy," in Proc. 2019 9th Int. Conf. Bioscience, Biochemistry and Bioinformatics, Singapore, pp. 17–21, Jan. 2019.
C. Mohanty et al., "Using deep learning architectures for detection and classification of diabetic retinopathy," Sensors, vol. 23, no. 12, p. 5726, 2023. doi: 10.3390/s23125726.
C. Wilkinson et al., "Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales," Ophthalmology, vol. 110, pp. 1677–1682, 2003.
I. Ardiyanto, H. A. Nugroho, and R. L. B. Buana, "Deep learning-based diabetic retinopathy assessment on embedded system," in Proc. 2017 39th Annu. Int. Conf. IEEE Eng. in Medicine and Biology Society (EMBC), Jeju Island, South Korea, pp. 1760–1763, Jul. 2017.
C. Harshitha et al., "Predicting the stages of diabetic retinopathy using deep learning," in Proc. 2021 6th Int. Conf. Inventive Computation Technologies (ICICT), Coimbatore, India, pp. 1–6, Jan. 2021.
X. Luo et al., "MVDRNet: Multi-view diabetic retinopathy detection by combining DCNNs and attention mechanisms," Pattern Recognit., vol. 120, p. 108104, 2021.
A. Kwasigroch, B. Jarzembinski, and M. Grochowski, "Deep CNN based decision support system for detection and assessing the stage of diabetic retinopathy," in Proc. 2018 Int. Interdisciplinary PhD Workshop (IIPhDW), Swinoujscie, Poland, pp. 111–116, May 2018.
R. Rajkumar, T. Jagathishkumar, D. Ragul, and A. G. Selvarani, "Transfer learning approach for diabetic retinopathy detection using residual network," in Proc. 2021 6th Int. Conf. Inventive Computation Technologies (ICICT), Coimbatore, India, Jan. 20–22, 2021, pp. 1189–1193.
S. B. Hathwar and G. Srinivasa, "Automated grading of diabetic retinopathy in retinal fundus images using deep learning," in Proc. 2019 IEEE Int. Conf. Signal and Image Processing Applications (ICSIPA), Kuala Lumpur, Malaysia, Sep. 17–19, 2019, pp. 73–77.
M. Oulhadj et al., "Diabetic retinopathy prediction based on deep learning and deformable registration," Multimedia Tools Appl., vol. 81, pp. 28709–28727, 2022. [Online]. Available: https://doi.org/10.1007/s11042-021-10579-4
J. Vaishnavi, S. Ravi, and A. Anbarasi, "An efficient adaptive histogram based segmentation and extraction model for the classification of severities on diabetic retinopathy," Multimedia Tools Appl., vol. 79, pp. 30439–30452, 2020. [Online]. Available: https://doi.org/10.1007/s11042-019-7585-6
H. Jiang, K. Yang, M. Gao, D. Zhang, H. Ma, and W. Qian, "An interpretable ensemble deep learning model for diabetic retinopathy disease classification," in Proc. 2019 41st Annu. Int. Conf. IEEE Eng. in Medicine and Biology Society (EMBC), Berlin, Germany, Jul. 23–27, 2019, pp. 2045–2048.
E. AbdelMaksoud, S. Barakat, and M. Elmogy, "Diabetic Retinopathy grading based on a hybrid deep learning model," in Proc. 2020 Int. Conf. Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), Sakheer, Bahrain, Oct. 26–27, 2020, pp. 1–6.
Z. Qian, C. Wu, H. Chen, and M. Chen, "Diabetic retinopathy grading using attention based convolution neural network," in Proc. 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conf. (IAEAC), Chongqing, China, Mar. 12–14, 2021, vol. 5, pp. 2652–2655.
R. Reguant, S. Brunak, and S. Saha, "Understanding inherent image features in CNN-based assessment of diabetic retinopathy," Sci. Rep., vol. 11, pp. 1–12, 2021. [Online]. Available: https://doi.org/10.1038/s41598-021-91261-7
X. Wang, Y. Lu, Y. Wang, and W. B. Chen, "Diabetic retinopathy stage classification using convolutional neural networks," in Proc. 2018 IEEE Int. Conf. Information Reuse and Integration (IRI), Salt Lake City, UT, USA, Jul. 7–9, 2018, pp. 465–471.
C. Y. Chen and M. C. Chang, "Using deep neural networks to classify the severity of diabetic retinopathy," in Proc. 2022 IEEE Int. Conf. Consumer Electronics-Taiwan (ICCE-TW), Taipei, Taiwan, Jul. 6–8, 2022, pp. 241–242.
M. Nithiyasri, G. Ananthi, and S. Thiruvengadam, "Improved classification of stages in diabetic retinopathy disease using deep learning algorithm," in Proc. 2022 Int. Conf. Wireless Communications Signal Processing and Networking (WiSPNET), Chennai, India, Mar. 24–26, 2022, pp. 143–147.
E. AbdelMaksoud, S. Barakat, and M. Elmogy, "A computer-aided diagnosis system for detecting various diabetic retinopathy grades based on a hybrid deep learning technique," Med. Biol. Eng. Comput., vol. 60, pp. 2015–2038, 2022. [Online]. Available: https://doi.org/10.1007/s11517-022-02535-3
J. Jaskari et al., "Uncertainty-aware deep learning methods for robust diabetic retinopathy classification," IEEE Access, vol. 10, pp. 76669–76681, 2022. [Online]. Available: https://doi.org/10.1109/ACCESS.2022.3193507
C. S. Teoh et al., "Variability in grading diabetic retinopathy using retinal photography and its comparison with an automated deep learning diabetic retinopathy screening software," Healthcare (Basel, Switzerland), vol. 11, no. 12, pp. 1697, Jun. 2023. [Online]. Available: https://doi.org/10.3390/healthcare11121697
Z. Hai et al., "A novel approach for intelligent diagnosis and grading of diabetic retinopathy," Comput. Biol. Med., vol. 172, 108246, 2024. [Online]. Available: https://doi.org/10.1016/j.compbiomed.2024.108246
F. Monteiro, "Diabetic retinopathy grading using blended deep learning," Procedia Comput. Sci., vol. 219, pp. 1097–1104, 2023. [Online]. Available: https://doi.org/10.1016/j.procs.2023.01.389
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