Hybrid generative model for grading the severity of diabetic retinopathy images

References

• Akram MU, Khalid S, Tariq A, Khan SA, Azam F. 2014. Detection and classification of retinal lesions for grading of diabetic retinopathy. Comput Biol Med. 45:161–171. https://doi.org/10.1016/j.compbiomed.2013.11.014.
   PubMed Web of Science ®Google Scholar
• Akyol K, Bayir S, Baha S. 2017. A decision support system for early-stage diabetic retinopathy lesions. Inter J Adv Comput Sci Appl. 8(12). doi: 10.14569/IJACSA.2017.081249.
   Google Scholar
• Al-Hazaimeh OM, Abu-Ein AA, Tahat NM, Al-Smadi MM, Al-Nawashi MM. 2022 Dec 2. Combining artificial intelligence and image processing for diagnosing diabetic retinopathy in retinal fundus images. Int J Onl Eng. 18(13):131–151. 10.3991/ijoe.v18i13.33985
   Google Scholar
• Aljehane NO An intelligent moth flame optimization with inception network for diabetic retinopathy detection and grading. 2022 2nd International Conference on Computing and Information Technology (ICCIT). Bangladesh: IEEE.
   Google Scholar
• Anand D, Arulselvi G, Balaji GN, Chandra GR. 2022. A deep convolutional extreme machine learning classification method to detect bone cancer from histopathological images. Int J Intell Sys Appl Eng. 10(4):39–47.
   Google Scholar
• Anoop B 2022. Binary classification of DR-diabetic retinopathy using CNN with fundus colour images. Materials Today: Proceedings. 58:212–216.
   Google Scholar
• Appe SRN, Arulselvi G, Balaji GN. 2023. Tomato ripeness detection and classification using VGG based CNN models. Int J Intell Sys Appl Eng. 11(1):296–302.
   Google Scholar
• Arifoglu D, Bouchachia A. 2019. Detection of abnormal behaviour for dementia sufferers using convolutional neural networks. Artif Intell Med. 94:88–95. doi: 10.1016/j.artmed.2019.01.005.
   PubMed Web of Science ®Google Scholar
• Asgharzadeh-Bonab A, Amirani MC, Mehri A. 2020. Spectral entropy and deep convolutional neural network for ECG beat classification. Biocybern Biomed Eng. 40(2):691–700. doi: 10.1016/j.bbe.2020.02.004.
   Web of Science ®Google Scholar
• Aziz T, Charoenlarpnopparut C, Mahapakulchai S. 2023. Deep learning-based hemorrhage detection for diabetic retinopathy screening. Sci Rep. 13(1):1479. doi: 10.1038/s41598-023-28680-3.
   PubMed Web of Science ®Google Scholar
• Babu MJ, Geetha P, Soman K. 2016. Classification of remotely sensed algal blooms along the coast of India using support vector machines and regularized least squares. Ind J Sci Technol. 9(30):30. doi: 10.17485/ijst/2016/v9i30/99001.
   Google Scholar
• Basha AJ, Balaji BS, Poornima S, Prathilothamai M, Venkatachalam K. 2021. RETRACTED ARTICLE: support vector machine and simple recurrent network based automatic sleep stage classification of fuzzy kernel. J Ambient Intell Humaniz Comput. 12(6):6189–6197. doi: 10.1007/s12652-020-02188-4.
   Web of Science ®Google Scholar
• Bhattacharya S, Sehgal J, Issac A, MK D, Burget R, Kolarik M Computer vision method for grading of health of a fundus image on basis of presence of red lesions. 2018 41st International Conference on Telecommunications and Signal Processing (TSP); Athens, Greece; 2018: IEEE.
   Google Scholar
• Bhuvaneswari R, Ganesh Vaidyanathan S. 2021. Classification and grading of diabetic retinopathy images using mixture of ensemble classifiers. J Intell Fuzzy Syst. 41(6):7407–7419. doi: 10.3233/JIFS-211364.
   Web of Science ®Google Scholar
• Butt MM, Latif G, Iskandar DA, Alghazo J, Khan AH. 2019. Multi-channel convolutions neural network based diabetic retinopathy detection from fundus images. Procedia Comput Sci. 163:283–291. doi: 10.1016/j.procs.2019.12.110.
   Google Scholar
• Cordero-Martínez R, Sánchez D, Melin P. 2023. Optimizing a convolutional neural network with a hierarchical Genetic algorithm for diabetic retinopathy detection. In Fuzzy logic and neural networks for hybrid intelligent system design. Springer International Publishing. pp. 199–208. doi: 10.1007/978-3-031-22042-5_11.
   Google Scholar
• Damodaran N, Sowmya V, Govind D, KP S. Vol 1; 2019: Springer.
   Google Scholar
• Decenciere E, Cazuguel G, Zhang X, Thibault G, Klein J-C, Meyer F, Marcotegui B, Quellec G, Lamard M, Danno R. 2013. TeleOphta: machine learning and image processing methods for teleophthalmology. Irbm. 34(2):196–203. doi: 10.1016/j.irbm.2013.01.010.
   Web of Science ®Google Scholar
• Decencière E, Zhang X, Cazuguel G, Lay B, Cochener B, Trone C, Gain P, Ordonez R, Massin P, Erginay A. 2014. Feedback on a publicly distributed image database: the Messidor database. Image Anal Stereol. 33(3):231–234. doi: 10.5566/ias.1155.
   Web of Science ®Google Scholar
• Ding S, Wang H, Lu H, Nappi M, Wan S. 2023. Two path gland segmentation algorithm of colon pathological image based on local semantic guidance. IEEE J Biomed Health Inform. April 27(4):1701–1708. 10.1109/JBHI.2022.3207874
   PubMed Web of Science ®Google Scholar
• Duh EJ. 2008. Diabetic Retinopathy. 353–373.
   Google Scholar
• Fang L, Qiao H. 2022. Diabetic retinopathy classification using a novel DAG network based on multi-feature of fundus images. Biomed Signal Process Control. 77:103810. doi: 10.1016/j.bspc.2022.103810.
   Web of Science ®Google Scholar
• Gayathri S, Krishna AK, Gopi VP, Palanisamy P. 2020. Automated binary and multiclass classification of diabetic retinopathy using haralick and multiresolution features. IEEE Acces. 8:57497–57504. doi: 10.1109/ACCESS.2020.2979753.
   Web of Science ®Google Scholar
• Gharaibeh N, Abu-Ein AA, Al-Hazaimeh OM, Nahar KM, Abu-Ain WA, Al-Nawashi MM. 2023. Swin transformer-based segmentation and multi-scale feature pyramid fusion module for Alzheimer’s disease with machine learning. Int J Online Biomed Eng. 19(4):22–50.
   Web of Science ®Google Scholar
• Gharaibeh N, Al-Hazaimeh OM, Abu-Ein A, Nahar KM. 2021. A hybrid SVM NAÏVE-BAYES classifier for bright lesions recognition in eye fundus images. Int J Electr Eng Inform. 13(3):530–545.
   Google Scholar
• Gharaibeh N, Al-Hazaimeh OM, Al-Naami B, Nahar KM. 2018. An effective image processing method for detection of diabetic retinopathy diseases from retinal fundus images. Int J Signal Imag Syst Eng. 11(4):206–216.
   Web of Science ®Google Scholar
• Gundluru N, Rajput DS, Lakshmanna K, Kaluri R, Shorfuzzaman M, Uddin M, Rahman Khan MA. 2022. Enhancement of detection of diabetic retinopathy using Harris hawks optimization with deep learning model. Comput Intell Neurosci. 2022:1–13. doi: 10.1155/2022/8512469.
   Web of Science ®Google Scholar
• Guo L, Sim G, Matuszewski B. 2019. Inter-patient ECG classification with convolutional and recurrent neural networks. Biocybern Biomed Eng. 39(3):868–879. doi: 10.1016/j.bbe.2019.06.001.
   Web of Science ®Google Scholar
• He A, Li T, Li N, Wang K, Fu H. 2020. Cabnet: category attention block for imbalanced diabetic retinopathy grading. IEEE Trans Med Imaging. 40(1):143–153. doi: 10.1109/TMI.2020.3023463.
   PubMed Web of Science ®Google Scholar
• Huang S, Li J, Xiao Y, Shen N, Xu T. 2022. Rtnet: relation transformer network for diabetic retinopathy multi-lesion segmentation. IEEE Trans Med Imaging. 41(6):1596–1607. doi: 10.1109/TMI.2022.3143833.
   PubMed Web of Science ®Google Scholar
• Huang W, Zhang Y, Wan S. 2022. A sorting fuzzy min-max model in an embedded system for atrial fibrillation detection. ACM Trans Multimedia Comput Commun Appl. 18(2s):1–18. doi: 10.1145/3554737.
   Google Scholar
• Izzaty AMK, Cenggoro TW, Elwirehardja GN, Pardamean B. 2022. Multiclass classification of histology on colorectal cancer using deep learning. Commun Math Biol Neurosci. 1:224–230 .
   Google Scholar
• Jiang H, Yang K, Gao M, Zhang D, Ma H, Qian W An interpretable ensemble deep learning model for diabetic retinopathy disease classification. 2019 41st annual international conference of the IEEE engineering in medicine and biology society (EMBC); Berlin, German; 2019: IEEE.
   Google Scholar
• Kamran SA, Hossain KF, Tavakkoli A, Zuckerbrod SL, Baker SA. 2021. Vtgan: semi-supervised retinal image synthesis and disease prediction using vision transformers. Proceedings of the IEEE/CVF International Conference on Computer Vision. 1:3228–3238.
   Google Scholar
• Karthikeyan R, Alli P. 2018. Feature selection and parameters optimization of support vector machines based on hybrid glowworm swarm optimization for classification of diabetic retinopathy. J Med Syst. 42(10):1–11. doi: 10.1007/s10916-018-1055-x.
   Web of Science ®Google Scholar
• Kartik P, Sumanth KB, Sri Ram V, Jeyakumar G, Thampi SM, El-Alfy ESM, Trajkovic L. 2021. Decoding of graphically encoded numerical digits using deep learning and edge detection techniques. J Intell Fuzzy Syst. 41(5):5367–5374. doi: 10.3233/JIFS-189859.
   Web of Science ®Google Scholar
• Kumar S, Mankame DP. 2020. Optimization driven deep convolution neural network for brain tumor classification. Biocybern Biomed Eng. 40(3):1190–1204. doi: 10.1016/j.bbe.2020.05.009.
   Web of Science ®Google Scholar
• Lahmiri S. 2017. High‐frequency‐based features for low and high retina haemorrhage classification. Healthc Technol Lett. 4(1):20–24. doi: 10.1049/htl.2016.0067.
   PubMedGoogle Scholar
• Liming L, Xin D, Renjie L, Anjun H. 2023. Classification algorithm of retinopathy based on attention mechanism and multi feature fusion. Opto-Electron Eng. 50:220199-1–220199–12.
   Google Scholar
• Liu Y-P, Li Z, Xu C, Li J, Liang R. 2019. Referable diabetic retinopathy identification from eye fundus images with weighted path for convolutional neural network. Artif Intell Med. 99:101694. doi: 10.1016/j.artmed.2019.07.002.
   PubMed Web of Science ®Google Scholar
• Li J, Wu Y, Shen N, Zhang J, Chen E, Sun J, Deng Z, Zhang Y. 2020. A fully automatic computer-aided diagnosis system for hepatocellular carcinoma using convolutional neural networks. Biocybern Biomed Eng. 40(1):238–248. doi: 10.1016/j.bbe.2019.05.008.
   Web of Science ®Google Scholar
• Li Y-H, Yeh N-N, Chen S-J, Chung Y-C. 2019. Computer-assisted diagnosis for diabetic retinopathy based on fundus images using deep convolutional neural network. Mob Inf Syst. 2019:1–14. doi: 10.1155/2019/6142839.
   Web of Science ®Google Scholar
• Muljo HH, Pardamean B, Purwandari K, Cenggoro TW. 2022. Improving lung disease detection by joint learning with COVID-19 radiography database. Commun Math Biol Neurosci. 2022:1.
   Google Scholar
• Nahiduzzaman M, Islam MR, Goni MOF, Anower MS, Ahsan M, Haider J, Kowalski M. 2023. Diabetic retinopathy identification using parallel convolutional neural network based feature extractor and ELM classifier. Expert systems with Applications.119557. Expert Syst Appl. 217:119557. doi: 10.1016/j.eswa.2023.119557.
   Web of Science ®Google Scholar
• Natarajan B Elakkiya R Bhuvaneswari R Saleem K Chaudhary D Samsudeen SH2023Creating alert messages based on wild animal activity detection using hybrid deep neural networks
   Google Scholar
• Ni B, Liu Z, Cai X, et al. 2023. Segmentation of ultrasound image sequences by combing a novel deep siamese network with a deformable contour model. Neural Comput Applic. 35:14535–14549. doi: 10.1007/s00521-022-07054-2.
   Web of Science ®Google Scholar
• Pao S-I, Lin H-Z, Chien K-H, Tai M-C, Chen J-T, Lin G-M. 2020. Detection of diabetic retinopathy using bichannel convolutional neural network. J Ophthalmol. 2020:1–7. doi: 10.1155/2020/9139713.
   Web of Science ®Google Scholar
• Pascolini, D. Donatella, Silvio P. M. 2012. Global estimates of visual impairment: 2010 British Journal of Ophthalmology.96:614–618.
   PubMed Web of Science ®Google Scholar
• Prabha, S, Reddy, M. A, Sreehari, M, Sankaran, K. S. 2022. Machine Learning Technique for Analysing the Diabetic Retinopathy. International Conference on Sustainable Computing and Data Communication Systems (ICSCDS); Erode, Tamil Nadu, India; IEEE.
   Google Scholar
• Roychowdhury S, Koozekanani DD, Parhi KK. 2013. DREAM: diabetic retinopathy analysis using machine learning. IEEE J Biomed Health Inform. 18(5):1717–1728. doi: 10.1109/JBHI.2013.2294635.
   Web of Science ®Google Scholar
• Sahlsten J, Jaskari J, Kivinen J, Turunen L, Jaanio E, Hietala K, Kaski K. 2019. Deep learning fundus image analysis for diabetic retinopathy and macular edema grading. Sci Rep. 9(1):10750. doi: 10.1038/s41598-019-47181-w.
   PubMedGoogle Scholar
• Sengan S, Arokia Jesu Prabhu L, Ramachandran V, Priya V, Ravi L, Subramaniyaswamy V, Varadarajan V, Kommers P, Piuri V, Subramaniyaswamy V. 2020. Images super-resolution by optimal deep AlexNet architecture for medical application: a novel DOCALN. J Intell Fuzzy Syst. 39(6):8259–8272. doi: 10.3233/JIFS-189146.
   Web of Science ®Google Scholar
• Sountharrajan S, Suganya E, Karthiga M, Rajan C. 2018. Automatic glioblastoma multiforme detection using hybrid-SVM with improved particle swarm optimisation. Int J Biomed Eng Technol. 26(3–4):353–364. doi: 10.1504/IJBET.2018.089969.
   Web of Science ®Google Scholar
• Stitson M, Weston J, Gammerman A, Vovk V, Vapnik V. 1996. Theory of support vector machines. Univ London. 117(827):188–191.
   Google Scholar
• Wu Y, Kong Q, Zhang L, Castiglione A, Nappi M and Wan S, “CDT-CAD: context-aware deformable transformers for end-to-end chest abnormality detection on X-Ray images,” in IEEE/ACM Transactions on Computational Biology and Bioinformatics, doi: 10.1109/TCBB.2023.3258455.
   Google Scholar
• Wu H, Xie P, Zhang H, Li D, Cheng M. 2020. Predict pneumonia with chest X-ray images based on convolutional deep neural learning networks. J Intell Fuzzy Syst. 39(3):2893–2907. doi: 10.3233/JIFS-191438.
   Web of Science ®Google Scholar
• Yu S, Ma K, Bi Q, Bian C, Ning M, He N, Li Y, Liu H, Zheng Y Mil-vt: multiple instance learning enhanced vision transformer for fundus image classification. Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, 2021: Springer.
   Google Scholar