Nazih W, Aseeri AO, Atallah OY, El-Sappagh S. Vision transformer model for predicting the severity of diabetic retinopathy in fundus photography-based retina images. IEEE Access. 2023;11:117546.
Jabbar A, Naseem S, Li J, Mahmood T, Jabbar MK, Rehman A, Saba T. Deep transfer learning-based automated diabetic retinopathy detection using retinal fundus images in remote areas. Int J Comput Intell Syst. 2024;17(1): 135.
Rajalakshmi R, Mohammed R, Vengatesan K, PramodKumar TA, Venkatesan U, Usha M, Arulmalar S, Prathiba V, Mohan V. Wide-field imaging with smartphone based fundus camera: grading of severity of diabetic retinopathy and locating peripheral lesions in diabetic retinopathy. Eye (Lond). 2024;38(8):1471–6.
Article PubMed PubMed Central Google Scholar
Butt MM, Iskandar DA, Abdelhamid SE, Latif G, Alghazo R. Diabetic retinopathy detection from fundus images of the eye using hybrid deep learning features. Diagnostics (Basel). 2022;12(7): 1607.
Article PubMed PubMed Central Google Scholar
Shamrat FJ, Shakil R, Akter B, Ahmed MZ, Ahmed K, Bui FM, Moni MA. An advanced deep neural network for fundus image analysis and enhancing diabetic retinopathy detection. Healthc Analytics. 2024;5: 100303.
Skouta A, Elmoufidi A, Jai-Andaloussi S, Ouchetto O. Hemorrhage semantic segmentation in fundus images for the diagnosis of diabetic retinopathy by using a convolutional neural network. J Big Data. 2022;9(1): 78.
Liu Z, Han X, Gao L, Chen S, Huang W, Li P, Wu Z, Wang M, Zheng Y. Cost-effectiveness of incorporating self-imaging optical coherence tomography into fundus photography-based diabetic retinopathy screening. NPJ Digit Med. 2024;7(1):225.
Article PubMed PubMed Central Google Scholar
Gogulamudi N, Golla M, Kautish S, Almazyad AS, Xiong G, Mohamed AW. Evaluating the performance of a non-uniform squash function in Capsule networks for early diabetic retinopathy detection using fundus image analysis. Results Eng. 2024;23: 102820.
Ali A, Qadri S, Khan Mashwani W, Kumam W, Kumam P, Naeem S, Goktas A, Jamal F, Chesneau C, Anam S, Sulaiman M. Machine learning based automated segmentation and hybrid feature analysis for diabetic retinopathy classification using fundus image. Entropy Basel. 2020;22(5): 567.
Article PubMed PubMed Central Google Scholar
Xiao Y, Dan H, Du X, Michaelide M, Nie X, Wang W, Zheng M, Wang D, Huang Z, Song Z. Assessment of early diabetic retinopathy severity using ultra-widefield Clarus versus conventional five-field and ultra-widefield Optos fundus imaging. Sci Rep. 2023;13(1): 17131.
Article CAS PubMed PubMed Central Google Scholar
Araújo T, Aresta G, Mendonça L, Penas S, Maia C, Carneiro A, et al. Data augmentation for improving proliferative diabetic retinopathy detection in eye fundus images. IEEE Access. 2020;8:182462–74.
Shankar K, Zhang Y, Liu Y, Wu L, Chen CH. Hyperparameter tuning deep learning for diabetic retinopathy fundus image classification. IEEE Access. 2020;8:118164–73.
Sharma Y, Kaur R, Khan B, Chaturvedi GD, Sharma A. Diabetic retinopathy screening using MII Ret Cam assisted smartphone-based fundus imaging. J Med Surg Public Health. 2024;2: 100068.
Shankar K, Sait AR, Gupta D, Lakshmanaprabu SK, Khanna A, Pandey HM. Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model. Pattern Recognit Lett. 2020;133:210–6.
Palaniswamy T, Vellingiri M. Internet of things and deep learning enabled diabetic retinopathy diagnosis using retinal fundus images. IEEE Access. 2023;11:27590–601.
Vinayaki VD, Kalaiselvi RJ. Multithreshold image segmentation technique using remora optimization algorithm for diabetic retinopathy detection from fundus images. Neural Process Lett. 2022;54(3):2363–84.
Article PubMed PubMed Central Google Scholar
Kaushik H, Singh D, Kaur M, Alshazly H, Zaguia A, Hamam H. Diabetic retinopathy diagnosis from fundus images using stacked generalization of deep models. IEEE Access. 2021;9:108276–92.
Shi D, Zhang W, He S, Chen Y, Song F, Liu S, Wang R, Zheng Y, He M. Translation of color fundus photography into fluorescein angiography using deep learning for enhanced diabetic retinopathy screening. Ophthalmol Sci. 2023;3(4): 100401.
Article PubMed PubMed Central Google Scholar
Sahlsten J, Jaskari J, Kivinen J, Turunen L, Jaanio E, Hietala K, Kaski K. Deep learning fundus image analysis for diabetic retinopathy and macular edema grading. Sci Rep. 2019;9(1): 10750.
Article PubMed PubMed Central Google Scholar
Mahmood MA, Aktar N, Kader MF. A hybrid approach for diagnosing diabetic retinopathy from fundus image exploiting deep features. Heliyon. 2023. https://doi.org/10.1016/j.heliyon.2023.e19625.
Article PubMed PubMed Central Google Scholar
Bhardwaj C, Jain S, Sood M. Deep learning–based diabetic retinopathy severity grading system employing quadrant ensemble model. J Digit Imaging. 2021;34(2):440–57.
Article PubMed PubMed Central Google Scholar
Islam MR, Abdulrazak LF, Nahiduzzaman M, Goni MO, Anower MS, Ahsan M, Haider J, Kowalski M. Applying supervised contrastive learning for the detection of diabetic retinopathy and its severity levels from fundus images. Comput Biol Med. 2022;146: 105602.
Bhardwaj C, Jain S, Sood M. Transfer learning based robust automatic detection system for diabetic retinopathy grading. Neural Comput Appl. 2021;33(20):13999–4019.
Chakraborty S, Jana GC, Kumari D, Swetapadma A. An improved method using supervised learning technique for diabetic retinopathy detection. Int J Inf Technol. 2020;12(2):473–7.
Alshahrani M, Al-Jabbar M, Senan EM, Ahmed IA, Saif JA. Hybrid methods for fundus image analysis for diagnosis of diabetic retinopathy development stages based on fusion features. Diagnostics (Basel, Switzerland). 2023;13(17): 2783.
PubMed PubMed Central Google Scholar
Bodapati JD, Shaik NS, Naralasetti V. Composite deep neural network with gated-attention mechanism for diabetic retinopathy severity classification. J Ambient Intell Humaniz Comput. 2021;12(10):9825–39.
Jagadesh BN, Karthik MG, Siri D, Shareef SK, Mantena SV, Vatambeti R. Segmentation using the IC2T model and classification of diabetic retinopathy using the rock hyrax swarm-based coordination attention mechanism. IEEE Access. 2023;11:124441–58.
Oh K, Kang HM, Leem D, Lee H, Seo KY, Yoon S. Early detection of diabetic retinopathy based on deep learning and ultra-wide-field fundus images. Sci Rep. 2021;11(1):1897.
Article CAS PubMed PubMed Central Google Scholar
Shahzad T, Saleem M, Farooq MS, Abbas S, Khan MA, Ouahada K. Developing a transparent diagnosis model for diabetic retinopathy using explainable AI. IEEE Access. 2024. https://doi.org/10.1109/ACCESS.2024.3475550.
Phridviraj MS, Bhukya R, Madugula S, Manjula A, Vodithala S, Waseem MS. A bi-directional long short-term memory-based diabetic retinopathy detection model using retinal fundus images. Healthc Anal. 2023;3: 100174.
Ghouali S, Onyema EM, Guellil MS, Wajid MA, Clare O, Cherifi W, Feham M. Artificial intelligence-based teleopthalmology application for diagnosis of diabetics retinopathy. IEEE Open J Eng Med Biol. 2022;3:124–33.
Article CAS PubMed PubMed Central Google Scholar
Nazir K, Kim J, Byun YC. Enhancing early-stage diabetic retinopathy detection using a weighted ensemble of deep neural networks. IEEE Access. 2024. https://doi.org/10.1109/ACCESS.2024.3432867.
Ali G, Dastgir A, Iqbal MW, Anwar M, Faheem M. A hybrid convolutional neural network model for automatic diabetic retinopathy classification from fundus images. IEEE J Transl Eng Health Med. 2023;11:341–50.
Song Y, Liu J. An improved adaptive weighted median filter algorithm. In Journal of physics: conference series (vol. 1187, no. 4, p. 042107). IOP Publishing; 2019.
Almotairi S, Kareem G, Aouf M, Almutairi B, Salem MA. Liver tumor segmentation in CT scans using modified SegNet. Sensors (Basel, Switzerland). 2020;20(5): 1516.
Article PubMed PubMed Central Google Scholar
Saïdani A, Echi AK. Pyramid histogram of oriented gradient for machine-printed/handwritten and Arabic/Latin word discrimination. In: 2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR). 2014. p. 267–272.
Sultana F, Sufian A, Dutta P. Advancements in image classification using convolutional neural network. In: 2018 Fourth International Conference on Research in Computational intelligence and Communication Networks (ICRCICN). 2018. p. 122–129.
Bernardo LS, Damaševičius R, Ling SH, de Albuquerque VH, Tavares JM. Modified SqueezeNet architecture for Parkinson’s disease detection based on keypress data. Biomedicines. 2022;10(11):2746.
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