{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T02:47:02Z","timestamp":1747190822538,"version":"3.40.5"},"reference-count":36,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T00:00:00Z","timestamp":1655942400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational and Mathematical Methods in Medicine"],"published-print":{"date-parts":[[2022,6,23]]},"abstract":"<jats:p>Early diagnosis of retinal diseases such as diabetic retinopathy has had the attention of many researchers. Deep learning through the introduction of convolutional neural networks has become a prominent solution for image-related tasks such as classification and segmentation. Most tasks in image classification are handled by deep CNNs pretrained and evaluated on imagenet dataset. However, these models do not always translate to the best result on other datasets. Devising a neural network manually from scratch based on heuristics may not lead to an optimal model as there are numerous hyperparameters in play. In this paper, we use two nature-inspired swarm algorithms: particle swarm optimization (PSO) and ant colony optimization (ACO) to obtain TDCN models to perform classification of fundus images into severity classes. The power of swarm algorithms is used to search for various combinations of convolutional, pooling, and normalization layers to provide the best model for the task. It is observed that TDCN-PSO outperforms imagenet models and existing literature, while TDCN-ACO achieves faster architecture search. The best TDCN model achieves an accuracy of 90.3%, AUC ROC of 0.956, and a Cohen\u2019s kappa score of 0.967. The results were compared with the previous studies to show that the proposed TDCN models exhibit superior performance.<\/jats:p>","DOI":"10.1155\/2022\/3571364","type":"journal-article","created":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T23:05:29Z","timestamp":1656025529000},"page":"1-13","source":"Crossref","is-referenced-by-count":2,"title":["Early Diagnosis of Retinal Blood Vessel Damage via Deep Learning-Powered Collective Intelligence Models"],"prefix":"10.1155","volume":"2022","author":[{"given":"Pranjal","family":"Bhardwaj","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632 014, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0704-5127","authenticated-orcid":true,"given":"Prajjwal","family":"Gupta","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632 014, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8099-7183","authenticated-orcid":true,"given":"Thejineaswar","family":"Guhan","sequence":"additional","affiliation":[{"name":"School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632 014, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9352-0237","authenticated-orcid":true,"given":"Kathiravan","family":"Srinivasan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632 014, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2980942"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1007\/s11554-020-00987-8"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1007\/s40747-021-00324-x"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2021.016736"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1109\/ICCE-China.2017.7990985"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.2174\/1573399811666150330150038"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1109\/NEUREL.2016.7800141"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1109\/CRV.2010.38"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1109\/RTEICT.2017.8256708"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2903171"},{"key":"11","first-page":"147","article-title":"Automated detection of diabetic retinopathy using deep learning","volume":"2017","author":"C. Lam","year":"2018","journal-title":"AMIA Summits on Translational Science Proceedings"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1145\/3326172.3326198"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1088\/1749-4699\/8\/1\/014008"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330701"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-05318-5_4"},{"key":"16","article-title":"Practical Bayesian optimization of machine learning algorithms","volume":"25","author":"J. Snoek","year":"2012","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"1","key":"17","first-page":"26","article-title":"Hyperparameter optimization for machine learning models based on Bayesian optimization","volume":"17","author":"J. Wu","year":"2019","journal-title":"Journal of Electronic Science and Technology"},{"issue":"1","key":"18","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v29i1.9354","article-title":"Initializing Bayesian hyperparameter optimization via meta-learning","volume":"29","author":"M. Feurer","year":"2015","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"issue":"1","key":"19","first-page":"6765","article-title":"Hyperband: a novel bandit-based approach to hyperparameter optimization","volume":"18","author":"L. Li","year":"2017","journal-title":"The Journal of Machine Learning Research"},{"key":"20","first-page":"1437","article-title":"BOHB: robust and efficient hyperparameter optimization at scale","volume":"80","author":"S. Falkner","year":"2018","journal-title":"Proceedings of the 35th International Conference on Machine"},{"article-title":"Hyperparameter optimization: A spectral approach","year":"2017","author":"E. Hazan","key":"21"},{"key":"22","doi-asserted-by":"publisher","DOI":"10.1142\/S1469026818500086"},{"issue":"5","key":"23","doi-asserted-by":"crossref","first-page":"1515","DOI":"10.1109\/TPAMI.2019.2956703","article-title":"Dynamical hyperparameter optimization via deep reinforcement learning in tracking","volume":"43","author":"X. Dong","year":"2021","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"24","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.06.064"},{"key":"25","article-title":"Review of swarm intelligence-based feature selection methods","volume":"100, article 104210","author":"M. Rostami","year":"2020","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"26","doi-asserted-by":"publisher","DOI":"10.1109\/ICTCS.2019.8923027"},{"key":"27","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2881143"},{"key":"28","doi-asserted-by":"publisher","DOI":"10.1109\/HIS.2013.6920486"},{"key":"29","doi-asserted-by":"publisher","DOI":"10.1109\/NABIC.2009.5393783"},{"key":"30","doi-asserted-by":"publisher","DOI":"10.3390\/jpm12020317"},{"key":"31","first-page":"119","article-title":"Deepswarm: optimizing convolutional neural networks using swarm intelligence","volume-title":"UK Workshop on Computational Intelligence","author":"E. Byla","year":"2020"},{"key":"32","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2019.05.010"},{"key":"33","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0233514"},{"key":"34","doi-asserted-by":"publisher","DOI":"10.1109\/ISSPIT47144.2019.9001846"},{"key":"35","doi-asserted-by":"publisher","DOI":"10.1109\/TENCON50793.2020.9293739"},{"key":"36","doi-asserted-by":"publisher","DOI":"10.3390\/electronics9060914"}],"container-title":["Computational and Mathematical Methods in Medicine"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2022\/3571364.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2022\/3571364.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2022\/3571364.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T23:05:41Z","timestamp":1656025541000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/cmmm\/2022\/3571364\/"}},"subtitle":[],"editor":[{"given":"Pan","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2022,6,23]]},"references-count":36,"alternative-id":["3571364","3571364"],"URL":"https:\/\/doi.org\/10.1155\/2022\/3571364","relation":{},"ISSN":["1748-6718","1748-670X"],"issn-type":[{"type":"electronic","value":"1748-6718"},{"type":"print","value":"1748-670X"}],"subject":[],"published":{"date-parts":[[2022,6,23]]}}}