{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T12:52:56Z","timestamp":1785761576459,"version":"3.56.0"},"reference-count":40,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T00:00:00Z","timestamp":1785715200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100016047","name":"Trinity University and by the Science Fund of the Republic of Serbia","doi-asserted-by":"publisher","award":["7373"],"award-info":[{"award-number":["7373"]}],"id":[{"id":"10.13039\/501100016047","id-type":"DOI","asserted-by":"publisher"}]},{"award":["7373"],"award-info":[{"award-number":["7373"]}],"id":[{"id":"https:\/\/ror.org\/02r531a15","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biomimetics"],"abstract":"<jats:p>Colorectal cancer remains a leading cause of cancer-related mortality worldwide, with automated polyp classification from endoscopic images offering a promising avenue for improving early detection. Existing approaches rely on single convolutional neural network (CNN) backbones with manually designed classification heads, limiting both representational capacity and deployment flexibility. This paper presents a swarm intelligence-augmented multi-backbone deep learning framework for eight-class gastrointestinal lesion classification on the Kvasir benchmark. Four CNN backbones (ResNet50, DenseNet121, MobileNetV2, EfficientNetB3) are independently fine-tuned using a two-phase transfer learning protocol and their penultimate-layer features concatenated into a 5888-dimensional representation, reduced to 256 dimensions via PCA. Five swarm intelligence algorithms\u2014Particle Swarm Optimization, Artificial Bee Colony, JADE, L-SHADE, and CMA-ES\u2014are benchmarked on the classification head architecture search task; all independently converge to tanh activation, a consistent pattern across independently initialized algorithms that is suggestive of, though not conclusive evidence for, particular geometric properties of PCA-transformed deep feature spaces. The PSO-optimized single-layer head (284 units, tanh) outperforms a manually designed three-layer baseline by 0.75% while using 67% fewer parameters. SI-guided class weight optimization yields targeted F1 improvements on the two most clinically significant classes (polyps: +0.015, ulcerative-colitis: +0.013). The fixed-head classifier trained on fused four-backbone features achieves 91.08% accuracy on Kvasir v2 (multi-seed mean 91.47% \u00b1 0.49 across nine converging seeds; one seed failed to converge and is disclosed rather than excluded), below end-to-end DenseNet121 (92.25%; Wilcoxon p = 0.31, not statistically significant), while enabling classifier updates in under 30 s; a three-backbone subset dropping the weakest backbone (EfficientNetB3) reaches 92.33%, exceeding the full four-backbone fusion. Cross-dataset evaluation on Kvasir v1-to-v2 confirms near-zero generalization gaps across dataset scales; a restricted two-class evaluation on HyperKvasir (the only two of eight classes with usable labeled data) reaches 96.28% accuracy, and dual Grad-CAM with SI minimal sufficient region analysis, validated quantitatively against Kvasir-SEG ground-truth masks, provides spatially grounded, clinically interpretable explanations.<\/jats:p>","DOI":"10.3390\/biomimetics11080541","type":"journal-article","created":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T11:50:21Z","timestamp":1785757821000},"page":"541","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Swarm Intelligence-Guided Hybrid Transfer Learning for Gastrointestinal Polyp Classification"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-7317-0281","authenticated-orcid":false,"given":"Una","family":"Tuba","sequence":"first","affiliation":[{"name":"Singidunum University, 11000 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6136-1895","authenticated-orcid":false,"given":"Mladen","family":"Veinovic","sequence":"additional","affiliation":[{"name":"Singidunum University, 11000 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4866-9048","authenticated-orcid":false,"given":"Eva","family":"Tuba","sequence":"additional","affiliation":[{"name":"Singidunum University, 11000 Belgrade, Serbia"},{"name":"Jozef Stefan Institute, 1000 Ljubljana, Slovenia"},{"name":"D.R. Semmes School of Science, Trinity University, San Antonio, TX 78212, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0761-1667","authenticated-orcid":false,"given":"Adis","family":"Alihodzic","sequence":"additional","affiliation":[{"name":"Faculty of Science, University of Sarajevo, 71000 Sarajevo, Bosnia and Herzegovina"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3794-3056","authenticated-orcid":false,"given":"Milan","family":"Tuba","sequence":"additional","affiliation":[{"name":"Singidunum University, 11000 Belgrade, Serbia"},{"name":"Sinergija University, 76300 Bijeljina, Bosnia and Herzegovina"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1136\/gutjnl-2022-327736","article-title":"Global burden of colorectal cancer in 2020 and 2040: Incidence and mortality estimates from GLOBOCAN","volume":"72","author":"Morgan","year":"2023","journal-title":"Gut"},{"key":"ref_2","first-page":"229","article-title":"Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"74","author":"Bray","year":"2024","journal-title":"CA Cancer J. Clin."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1056\/NEJMoa1100370","article-title":"Colonoscopic polypectomy and long-term prevention of colorectal-cancer deaths","volume":"366","author":"Zauber","year":"2012","journal-title":"N. Engl. J. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1754","DOI":"10.14309\/ajg.0000000000002972","article-title":"Quality indicators for colonoscopy","volume":"119","author":"Rex","year":"2024","journal-title":"Off. J. Am. Coll. Gastroenterol. ACG"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Iman, M., Arabnia, H.R., and Rasheed, K. (2023). A review of deep transfer learning and recent advancements. Technologies, 11.","DOI":"10.3390\/technologies11020040"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Pogorelov, K., Randel, K.R., Griwodz, C., Eskeland, S.L., de Lange, T., Johansen, D., Spampinato, C., Dang-Nguyen, D.T., Lux, M., and Schmidt, P.T. (2017). KVASIR: A multi-class image dataset for computer aided gastrointestinal disease detection. Proceedings of the 8th ACM on Multimedia Systems Conference (MMSys), ACM.","DOI":"10.1145\/3193289"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Cristea, D.M., Onita, D., and Iantovics, L.B. (2025). Biomimetic transfer learning-based complex gastrointestinal polyp classification. Biomimetics, 10.","DOI":"10.3390\/biomimetics10100699"},{"key":"ref_9","first-page":"1942","article-title":"Particle swarm optimization","volume":"Volume 4","author":"Kennedy","year":"1995","journal-title":"Proceedings of the ICNN\u201995\u2014International Conference on Neural Networks, Perth, Australia, 27 November\u20131 December 1995"},{"key":"ref_10","unstructured":"Karaboga, D. (2005). An Idea Based on Honey Bee Swarm for Numerical Optimization, Erciyes University, Engineering Faculty, Computer Engineering Department. Technical Report TR06."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1280","DOI":"10.1109\/ACCESS.2022.3233596","article-title":"A survey on the optimization of artificial neural networks using swarm intelligence algorithms","volume":"11","author":"Emambocus","year":"2023","journal-title":"IEEE Access"},{"key":"ref_12","first-page":"e14942","article-title":"Comparative study of convolutional neural network architectures for gastrointestinal lesions classification","volume":"11","year":"2023","journal-title":"PeerJ"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2410","DOI":"10.1007\/s10489-022-03689-9","article-title":"A deep ensemble learning method for colorectal polyp classification with optimized network parameters","volume":"53","author":"Younas","year":"2023","journal-title":"Appl. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Gunasekaran, H., Ramalakshmi, K., Swaminathan, D.K., and Mazzara, M. (2023). GIT-Net: An ensemble deep learning-based GI tract classification of endoscopic images. Bioengineering, 10.","DOI":"10.3390\/bioengineering10070809"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mukhtorov, D., Rakhmonova, M., Muksimova, S., and Cho, Y.I. (2023). Endoscopic image classification based on explainable deep learning. Sensors, 23.","DOI":"10.3390\/s23063176"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bhardwaj, P., Kim, S., Koul, A., Kumar, Y., Changela, A., Shafi, J., and Ijaz, M.F. (2024). Advanced CNN models in gastric cancer diagnosis: Enhancing endoscopic image analysis with deep transfer learning. Front. Oncol., 14.","DOI":"10.3389\/fonc.2024.1431912"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Shen, M.H., Huang, C.C., Chen, Y.T., Tsai, Y.J., Liou, F.M., Chang, S.C., and Phan, N.N. (2023). Deep learning empowers endoscopic detection and polyps classification: A multiple-hospital study. Diagnostics, 13.","DOI":"10.3390\/diagnostics13081473"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Aguerchi, K., Jabrane, Y., Habba, M., and Hajjam El Hassani, A. (2024). A CNN hyperparameters optimization based on particle swarm optimization for mammography breast cancer classification. J. Imaging, 10.","DOI":"10.3390\/jimaging10020030"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Amoury, S., Smili, Y., and Fakhri, Y. (2025). Design of an optimal convolutional neural network architecture for MRI brain tumor classification by exploiting particle swarm optimization. J. Imaging, 11.","DOI":"10.3390\/jimaging11020031"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"37543","DOI":"10.1038\/s41598-025-23820-3","article-title":"A swarm intelligence-driven hybrid framework for brain tumor classification with enhanced deep features","volume":"15","author":"Yonar","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_21","unstructured":"Hamad, Q.S., Samma, H., and Suandi, S.A. (2022). Optimization of convolutional neural network hyperparameter for medical image diagnosis using metaheuristic algorithms: A short recent review (2019\u20132022). arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xu, M., Cao, L., Lu, D., Hu, Z., and Yue, Y. (2023). Application of swarm intelligence optimization algorithms in image processing: A comprehensive review of analysis, synthesis, and optimization. Biomimetics, 8.","DOI":"10.3390\/biomimetics8020235"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Cox, K., Haggerty, L., Abeln, K., Hernandez, L., Wicker, J., Padgett, L., and Privitera, M.B. (2022). Artificial intelligence for upper gastrointestinal endoscopy: A roadmap from technology development to clinical practice. Diagnostics, 12.","DOI":"10.3390\/diagnostics12051245"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2748","DOI":"10.1038\/s41598-022-06726-2","article-title":"Artificial intelligence enabled automated diagnosis and grading of ulcerative colitis endoscopy images","volume":"12","author":"Sutton","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"7935346","DOI":"10.1155\/2022\/7935346","article-title":"A framework for interactive medical image segmentation using optimized swarm intelligence with convolutional neural networks","volume":"2022","author":"Kaushal","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009). ImageNet: A large-scale hierarchical image database. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K.Q. (2017). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018). MobileNetV2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_30","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","volume":"97","author":"Tan","year":"2019","journal-title":"Proc. Mach. Learn. Res."},{"key":"ref_31","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A method for stochastic optimization. Proceedings of the 3rd International Conference on Learning Representations (ICLR), San Diego, CA, USA."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1109\/TEVC.2009.2014613","article-title":"JADE: Adaptive differential evolution with optional external archive","volume":"13","author":"Zhang","year":"2009","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Tanabe, R., and Fukunaga, A.S. (2014). Improving the search performance of SHADE using linear population size reduction. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), IEEE.","DOI":"10.1109\/CEC.2014.6900380"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1023\/A:1008202821328","article-title":"Differential evolution\u2014A simple and efficient heuristic for global optimization over continuous spaces","volume":"11","author":"Storn","year":"1997","journal-title":"J. Glob. Optim."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1162\/106365601750190398","article-title":"Completely derandomized self-adaptation in evolution strategies","volume":"9","author":"Hansen","year":"2001","journal-title":"Evol. Comput."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017). Grad-CAM: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1038\/s41597-020-00622-y","article-title":"HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy","volume":"7","author":"Borgli","year":"2020","journal-title":"Sci. Data"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Jha, D., Smedsrud, P.H., Riegler, M.A., Halvorsen, P., Lange, T.d., Johansen, D., and Johansen, H.D. (2020). Kvasir-SEG: A segmented polyp dataset. Proceedings of the International Conference on Multimedia Modeling, Springer.","DOI":"10.1007\/978-3-030-37734-2_37"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"80","DOI":"10.2307\/3001968","article-title":"Individual Comparisons by Ranking Methods","volume":"1","author":"Wilcoxon","year":"1945","journal-title":"Biom. Bull."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1038\/s42256-020-00257-z","article-title":"Shortcut learning in deep neural networks","volume":"2","author":"Geirhos","year":"2020","journal-title":"Nat. Mach. Intell."}],"container-title":["Biomimetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-7673\/11\/8\/541\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T12:19:57Z","timestamp":1785759597000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-7673\/11\/8\/541"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,3]]},"references-count":40,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["biomimetics11080541"],"URL":"https:\/\/doi.org\/10.3390\/biomimetics11080541","relation":{},"ISSN":["2313-7673"],"issn-type":[{"value":"2313-7673","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,3]]}}}