{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T13:36:05Z","timestamp":1793799365214,"version":"4.6.1"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T00:00:00Z","timestamp":1764288000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T00:00:00Z","timestamp":1767052800000},"content-version":"vor","delay-in-days":32,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-29447-8","type":"journal-article","created":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T04:36:31Z","timestamp":1764304591000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Hybrid deep learning with attention fusion for enhanced colon cancer detection"],"prefix":"10.1038","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7665-4428","authenticated-orcid":false,"given":"S\u00fcheyla Demirta\u015f","family":"Alpsalaz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0181-3658","authenticated-orcid":false,"given":"Emrah","family":"Aslan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8461-8702","authenticated-orcid":false,"given":"Y\u0131ld\u0131r\u0131m","family":"\u00d6z\u00fcpak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7695-6426","authenticated-orcid":false,"given":"Feyyaz","family":"Alpsalaz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8238-2588","authenticated-orcid":false,"given":"Hasan","family":"Uzel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Viktoria","family":"Bereznychenko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,28]]},"reference":[{"key":"29447_CR1","doi-asserted-by":"publisher","unstructured":"Mahmood, T., Saba, T. & Rehman, A. Breast cancer diagnosis with MFF-HistoNet: A multi-modal feature fusion network integrating CNNs and quantum tensor networks. J. Big Data. 12 (1), 1\u201335. https:\/\/doi.org\/10.1186\/S40537-025-01114-9\/FIGURES\/12 (2025).","DOI":"10.1186\/S40537-025-01114-9\/FIGURES\/12"},{"key":"29447_CR2","doi-asserted-by":"publisher","unstructured":"Mahmood, T. et al. Breast lesions classifications of mammographic images using a deep convolutional neural network-based approach. PLoS One. 17 (1), e0263126. https:\/\/doi.org\/10.1371\/JOURNAL.PONE.0263126 (Jan. 2022).","DOI":"10.1371\/JOURNAL.PONE.0263126"},{"key":"29447_CR3","doi-asserted-by":"publisher","unstructured":"Mahmood, T., Li, J., Pei, Y. & Akhtar, F. An automated in-depth feature learning algorithm for breast abnormality prognosis and robust characterization from mammography images using deep transfer learning. Biology 10(9), 859. https:\/\/doi.org\/10.3390\/BIOLOGY10090859 (2021).","DOI":"10.3390\/BIOLOGY10090859"},{"key":"29447_CR4","doi-asserted-by":"publisher","unstructured":"Singh, D. P., Banerjee, T., Kour, P., Swain, D. & Narayan, Y. CICADA (UCX): A novel approach for automated breast cancer classification through aggressiveness delineation. Comput. Biol. Chem. 115, 108368. https:\/\/doi.org\/10.1016\/J.COMPBIOLCHEM.2025.108368 (2025).","DOI":"10.1016\/J.COMPBIOLCHEM.2025.108368"},{"key":"29447_CR5","doi-asserted-by":"publisher","unstructured":"Singh, D. P., Kour, P., Banerjee, T. & Swain, D. A comprehensive review of various machine learning and deep learning models for anti-cancer drug response prediction: comparative analysis with existing state of the art methods. Arch. Comput. Methods Eng. 32 (6), 3733\u20133757. https:\/\/doi.org\/10.1007\/S11831-025-10255-2\/TABLES\/10 (2025).","DOI":"10.1007\/S11831-025-10255-2\/TABLES\/10"},{"key":"29447_CR6","doi-asserted-by":"publisher","unstructured":"Banerjee, T. et al. Pyramidal attention-based T network for brain tumor classification: A comprehensive analysis of transfer learning approaches for clinically reliable and reliable AI hybrid approaches. Sci. Rep. 15(1), 1\u201329 (2025). https:\/\/doi.org\/10.1038\/S41598-025-11574-X;SUBJMETA","DOI":"10.1038\/S41598-025-11574-X;SUBJMETA"},{"key":"29447_CR7","doi-asserted-by":"publisher","unstructured":"Banerjee, T. Towards automated and reliable lung cancer detection in histopathological images using DY-FSPAN: A feature-summarized pyramidal attention network for explainable AI. Comput. Biol. Chem. 118, 108500. https:\/\/doi.org\/10.1016\/J.COMPBIOLCHEM.2025.108500 (2025).","DOI":"10.1016\/J.COMPBIOLCHEM.2025.108500"},{"key":"29447_CR8","doi-asserted-by":"publisher","unstructured":"Banerjee, T. et al. Dec., A novel unified Inception-U-Net hybrid gravitational optimization model (UIGO) incorporating automated medical image segmentation and feature selection for liver tumor detection. Sci. Rep. 15(1),  1\u201337. https:\/\/doi.org\/10.1038\/S41598-025-14333-0;SUBJMETA (2025).","DOI":"10.1038\/S41598-025-14333-0;SUBJMETA"},{"key":"29447_CR9","doi-asserted-by":"publisher","unstructured":"Banerjee, T. et al. A novel hybrid deep learning approach combining deep feature attention and statistical validation for enhanced thyroid ultrasound segmentation. Sci. Rep. 15(1),  1\u201321 (2025). https:\/\/doi.org\/10.1038\/S41598-025-12602-6;SUBJMETA","DOI":"10.1038\/S41598-025-12602-6;SUBJMETA"},{"key":"29447_CR10","doi-asserted-by":"publisher","unstructured":"Pacal, I. & Banerjee, T. Towards accurate and interpretable brain tumor diagnosis: T-FSPANNet with tri-attribute and pyramidal attention-based feature fusion. Biomed. Signal Process Control  113(pt. A)  (2026). https:\/\/doi.org\/10.1016\/j.bspc.2025.108852","DOI":"10.1016\/j.bspc.2025.108852"},{"key":"29447_CR11","doi-asserted-by":"publisher","unstructured":"Schoenpflug, L. A. et al.  Tumour purity assessment with deep learning in colorectal cancer and impact on molecular analysis. J. Pathol. 265(2), 184\u2013197 (2025). https:\/\/doi.org\/10.1002\/PATH.6376","DOI":"10.1002\/PATH.6376"},{"key":"29447_CR12","doi-asserted-by":"publisher","unstructured":"Ukwuoma, C. C. et al. Enhancing histopathological medical image classification for early cancer diagnosis using deep learning and explainable AI \u2013 LIME & SHAP. Biomed. Signal. Process. Control. 100, 107014. https:\/\/doi.org\/10.1016\/J.BSPC.2024.107014 (2025).","DOI":"10.1016\/J.BSPC.2024.107014"},{"key":"29447_CR13","doi-asserted-by":"publisher","unstructured":"Alpsalaz, F., \u00d6z\u00fcpak, Y., Aslan, E. & Uzel, H. Classification of maize leaf diseases with deep learning: Performance evaluation of the proposed model and use of explicable artificial intelligence. Chemom. Intell. Lab. Syst. 262, 1\u201314  (2025). https:\/\/doi.org\/10.1016\/j.chemolab.2025.105412","DOI":"10.1016\/j.chemolab.2025.105412"},{"key":"29447_CR14","doi-asserted-by":"publisher","unstructured":"Gowthamy, J. & Ramesh, S. A novel hybrid model for lung and colon cancer detection using pre-trained deep learning and KELM. Expert Syst. Appl. 252, 124114. https:\/\/doi.org\/10.1016\/J.ESWA.2024.124114 (2024).","DOI":"10.1016\/J.ESWA.2024.124114"},{"key":"29447_CR15","doi-asserted-by":"publisher","unstructured":"Khalid, M., Deivasigamani, S. & Rajendran, S. S. V, and An efficient colorectal cancer detection network using atrous convolution with coordinate attention transformer and histopathological images. Sci. Rep. 14(1),  1\u201315  (2024). https:\/\/doi.org\/10.1038\/s41598-024-70117-y","DOI":"10.1038\/s41598-024-70117-y"},{"key":"29447_CR16","doi-asserted-by":"publisher","unstructured":"Talukder, M. A. et al. Machine learning-based lung and colon cancer detection using deep feature extraction and ensemble learning. Expert Syst. Appl. 205,  117695  (2022). https:\/\/doi.org\/10.1016\/J.ESWA.2022.117695","DOI":"10.1016\/J.ESWA.2022.117695"},{"key":"29447_CR17","doi-asserted-by":"publisher","unstructured":"Alpsalaz, S. D., Aslan, E., Uzel, H., Alpsalaz, F. & \u00d6z\u00fcpak, Y. Alzheimer\u2019s classification with a MaxViT-based deep learning model using magnetic resonance imaging. J. Appl. Sci. Technol. Trends (JASTT) 6(2),  316\u2013327  (2025). https:\/\/doi.org\/10.38094\/jastt62453","DOI":"10.38094\/jastt62453"},{"key":"29447_CR18","doi-asserted-by":"publisher","unstructured":"Pacal, I., Algarni, A., Bayram, B. & Ince, S. FA-UNet: A FasterNet and attention-gated hybrid network for precise ischemic stroke segmentation. J. Integr. Neurosci. 24 (10). https:\/\/doi.org\/10.31083\/JIN40100 (2025).","DOI":"10.31083\/JIN40100"},{"key":"29447_CR19","doi-asserted-by":"publisher","unstructured":"Su, Y. et al. Colon cancer diagnosis and staging classification based on machine learning and bioinformatics analysis. Comput. Biol. Med. 145, 105409. https:\/\/doi.org\/10.1016\/J.COMPBIOMED.2022.105409 (2022).","DOI":"10.1016\/J.COMPBIOMED.2022.105409"},{"key":"29447_CR20","doi-asserted-by":"publisher","unstructured":"Sakr, A. S. et al. An efficient deep learning approach for colon cancer detection. Appl. Sci. 12(17), 8450 (2022). https:\/\/doi.org\/10.3390\/APP12178450","DOI":"10.3390\/APP12178450"},{"key":"29447_CR21","doi-asserted-by":"publisher","unstructured":"Attallah, O., Aslan, M. F. & Sabanci, K. A framework for lung and colon cancer diagnosis via lightweight deep learning models and transformation methods. Diagnostics 12 (12), 2926. https:\/\/doi.org\/10.3390\/DIAGNOSTICS12122926 (2022).","DOI":"10.3390\/DIAGNOSTICS12122926"},{"key":"29447_CR22","doi-asserted-by":"publisher","unstructured":"Bokhorst, J. M. et al. Deep learning for multi-class semantic segmentation enables colorectal cancer detection and classification in digital pathology images. Sci. Rep. 13 (1), 1\u201317. https:\/\/doi.org\/10.1038\/S41598-023-35491-Z;SUBJMETA (2023).","DOI":"10.1038\/S41598-023-35491-Z;SUBJMETA"},{"key":"29447_CR23","doi-asserted-by":"publisher","unstructured":"Ben Hamida, A. et al. Deep learning for colon cancer histopathological images analysis. Comput. Biol. Med. 136, 104730. https:\/\/doi.org\/10.1016\/J.COMPBIOMED.2021.104730 (2021).","DOI":"10.1016\/J.COMPBIOMED.2021.104730"},{"key":"29447_CR24","doi-asserted-by":"publisher","unstructured":"Narayan, Y. et al. A comparative evaluation of deep learning architectures for prostate cancer segmentation: Introducing TrionixNet with N-core multi-attention mechanism. Arch. Comput. Methods Eng. 1\u201340. https:\/\/doi.org\/10.1007\/S11831-025-10411-8\/TABLES\/23 (2025).","DOI":"10.1007\/S11831-025-10411-8\/TABLES\/23"},{"key":"29447_CR25","doi-asserted-by":"publisher","unstructured":"Singh, D. P. et al. A comprehensive study of enhanced computational approaches for breast cancer classification: comparative analysis with existing state of the art methods. Arch. Comput. Methods Eng. 1\u201329. https:\/\/doi.org\/10.1007\/S11831-025-10414-5\/FIGURES\/16 (2025).","DOI":"10.1007\/S11831-025-10414-5\/FIGURES\/16"},{"key":"29447_CR26","doi-asserted-by":"publisher","unstructured":"Banerjee, T. Comparing bipartite convoluted and attention-driven methods for skin cancer detection: A review of explainable AI and transfer learning strategies. Arch. Comput. Methods Eng.. 1\u201325 (2025). https:\/\/doi.org\/10.1007\/S11831-025-10379-5\/FIGURES\/12","DOI":"10.1007\/S11831-025-10379-5\/FIGURES\/12"},{"key":"29447_CR27","doi-asserted-by":"publisher","unstructured":"Banerjee, T., Singh, D. P. & Kour, P. Advances in deep Neural, transformer Learning, and Kernel-Based methods for diabetic retinopathy detection: A comprehensive review. Arch. Comput. Methods Eng. 1\u201349. https:\/\/doi.org\/10.1007\/S11831-025-10376-8\/FIGURES\/27 (2025).","DOI":"10.1007\/S11831-025-10376-8\/FIGURES\/27"},{"key":"29447_CR28","doi-asserted-by":"publisher","unstructured":"Singh, D. P. et al. A comprehensive study on deep learning models for the detection of diabetic retinopathy using pathological images. Arch. Comput. Methods Eng. 1\u201330. https:\/\/doi.org\/10.1007\/S11831-025-10315-7\/TABLES\/20 (2025).","DOI":"10.1007\/S11831-025-10315-7\/TABLES\/20"},{"key":"29447_CR29","doi-asserted-by":"publisher","unstructured":"Rehman, A., Mahmood, T. & Saba, T. Robust kidney carcinoma prognosis and characterization using Swin-ViT and DeepLabV3\u2009+\u2009with multi-model transfer learning. Appl. Soft Comput. 170, 112518. https:\/\/doi.org\/10.1016\/J.ASOC.2024.112518 (2025).","DOI":"10.1016\/J.ASOC.2024.112518"},{"key":"29447_CR30","doi-asserted-by":"publisher","unstructured":"Mahmood, T., Saba, T., Rehman, A. & Alamri, F. S. Harnessing the power of radiomics and deep learning for improved breast cancer diagnosis with multiparametric breast mammography. Expert Syst. Appl. 249, 123747. https:\/\/doi.org\/10.1016\/J.ESWA.2024.123747 (2024).","DOI":"10.1016\/J.ESWA.2024.123747"},{"key":"29447_CR31","doi-asserted-by":"publisher","unstructured":"Mahmood, T., Rehman, A., Saba, T., Wang, Y. & Alamri, F. S. Alzheimer\u2019s disease unveiled: Cutting-edge multi-modal neuroimaging and computational methods for enhanced diagnosis. Biomed. Signal. Process. Control. 97, 106721. https:\/\/doi.org\/10.1016\/J.BSPC.2024.106721 (2024).","DOI":"10.1016\/J.BSPC.2024.106721"},{"key":"29447_CR32","doi-asserted-by":"publisher","unstructured":"Jo, K. et al. Machine learning-assisted label-free colorectal cancer diagnosis using plasmonic needle-endoscopy system. Biosens. Bioelectron. 264, 116633. https:\/\/doi.org\/10.1016\/J.BIOS.2024.116633 (2024).","DOI":"10.1016\/J.BIOS.2024.116633"},{"key":"29447_CR33","doi-asserted-by":"publisher","unstructured":"Shahadat, N., Lama, R. & Nguyen, A. Lung and colon cancer detection using a deep AI model. Cancers 16(22), 3879 (2024). https:\/\/doi.org\/10.3390\/CANCERS16223879","DOI":"10.3390\/CANCERS16223879"},{"key":"29447_CR34","doi-asserted-by":"publisher","unstructured":"Di Giammarco, M., Martinelli, F., Santone, A., Cesarelli, M. & Mercaldo, F. Colon cancer diagnosis by means of explainable deep learning. Sci. Rep. 14 (1), 1\u201311. https:\/\/doi.org\/10.1038\/S41598-024-63659-8;SUBJMETA (2024).","DOI":"10.1038\/S41598-024-63659-8;SUBJMETA"},{"key":"29447_CR35","doi-asserted-by":"publisher","unstructured":"Karthikeyan, A., Jothilakshmi, S. & Suthir, S. Colorectal cancer detection based on convolutional neural networks (CNN) and ranking algorithm. Meas. Sens. 31, 100976. https:\/\/doi.org\/10.1016\/J.MEASEN.2023.100976 (2024).","DOI":"10.1016\/J.MEASEN.2023.100976"},{"key":"29447_CR36","doi-asserted-by":"publisher","unstructured":"Junaid, H. H. S., Daneshfar, F. & Mohammad, M. A. Automatic colorectal cancer detection using machine learning and deep learning based on feature selection in histopathological images. Biomed. Signal. Process. Control. 107, 107866. https:\/\/doi.org\/10.1016\/J.BSPC.2025.107866 (2025).","DOI":"10.1016\/J.BSPC.2025.107866"},{"key":"29447_CR37","doi-asserted-by":"publisher","unstructured":"Gowthamy, J. & Ramesh, S. S. S. Augmented histopathology: Enhancing colon cancer detection through deep learning and ensemble techniques. Microsc Res. Tech. 88 (1), 298\u2013314. https:\/\/doi.org\/10.1002\/JEMT.24692 (2025).","DOI":"10.1002\/JEMT.24692"},{"key":"29447_CR38","doi-asserted-by":"publisher","unstructured":"Hai Minh, N., Quy, T. Q., Tam, N. D., Tuan, T. M. & Son, L. H. A practical approach for colorectal cancer diagnosis based on machine learning, PLoS One 20(4),  e0321009 (2025). https:\/\/doi.org\/10.1371\/JOURNAL.PONE.0321009","DOI":"10.1371\/JOURNAL.PONE.0321009"},{"key":"29447_CR39","unstructured":"The Kvasir Dataset. https:\/\/www.kaggle.com\/datasets\/yasserhessein\/the-kvasir-dataset. Accessed 06 Mar 2025."},{"key":"29447_CR40","doi-asserted-by":"publisher","first-page":"113618","DOI":"10.1109\/ACCESS.2025.3583773","volume":"13","author":"E Aslan","year":"2025","unstructured":"Aslan, E., \u00d6z\u00fcpak, Y., Alpsalaz, F. & Elbarbary, Z. M. S. A hybrid machine learning approach for predicting power transformer failures using internet of things-based monitoring and explainable artificial intelligence. IEEE Access. 13, 113618\u2013113633. https:\/\/doi.org\/10.1109\/ACCESS.2025.3583773 (2025).","journal-title":"IEEE Access."},{"key":"29447_CR41","doi-asserted-by":"publisher","unstructured":"\u00d6z\u00fcpak, Y. & Aslan, E. Using artificial neural networks to improve the efficiency of transformers used in wireless power transmission systems for different coil positions. Rev. Roumaine Sci. Tech. S\u00e9r. \u00c9lectrotech. \u00c9nerg.  69(2),  195\u2013200  (2024). https:\/\/doi.org\/10.59277\/RRST-EE.2024.2.13","DOI":"10.59277\/RRST-EE.2024.2.13"},{"key":"29447_CR42","doi-asserted-by":"publisher","unstructured":"Said, B. et al. Hybrid MPC\u2013third-order sliding mode control with MRAS for fault-tolerant speed regulation of PMSMs under sensor failures. Int. Trans. Electr. Energy Syst. 1,  5984024  (2025). https:\/\/doi.org\/10.1155\/ETEP\/5984024","DOI":"10.1155\/ETEP\/5984024"},{"key":"29447_CR43","doi-asserted-by":"publisher","unstructured":"Tharwat, M. et al. Colon cancer diagnosis based on machine learning and deep learning: Modalities and analysis techniques. Sensors 22(23),  9250 (2022). https:\/\/doi.org\/10.3390\/S22239250","DOI":"10.3390\/S22239250"},{"key":"29447_CR44","doi-asserted-by":"publisher","unstructured":"Said, B. et al. Advanced fault classification in induction motors for electric vehicles using a stacking ensemble learning approach. World Electr. Veh. J. 16(11),  1\u201325 (2025). https:\/\/doi.org\/10.3390\/wevj16110614","DOI":"10.3390\/wevj16110614"},{"key":"29447_CR45","doi-asserted-by":"publisher","unstructured":"An, F., Wang, J. & Liu, R. Road traffic sign recognition algorithm based on cascade attention-modulation fusion mechanism. IEEE Trans. Intell. Transp. Syst. 1\u201311. https:\/\/doi.org\/10.1109\/TITS.2024.3439699 (2024).","DOI":"10.1109\/TITS.2024.3439699"},{"key":"29447_CR46","doi-asserted-by":"publisher","unstructured":"Ismi, D. P., Pulungan, R. & Afiahayati Self-attention and asymmetric multi-layer perceptron-gated recurrent unit blocks for protein secondary structure prediction. Appl. Soft Comput. 159, 111604. https:\/\/doi.org\/10.1016\/J.ASOC.2024.111604 (2024).","DOI":"10.1016\/J.ASOC.2024.111604"},{"key":"29447_CR47","doi-asserted-by":"publisher","unstructured":"Aslan, E. Prediction and comparative analysis of emissions from gas turbines using random search optimization and different machinel earning based algorithms. Bull. Pol. Acad. Sci. Tech. Sci. 151956\u2013151956. https:\/\/doi.org\/10.24425\/BPASTS.2024.151956 ( 2024).","DOI":"10.24425\/BPASTS.2024.151956"},{"key":"29447_CR48","doi-asserted-by":"publisher","unstructured":"Aslan, E. Temperature prediction and performance comparison of permanent magnet synchronous motors using different machine learning techniques for early failure detection. Maintenance Reliab. \/ Eksploatacja Niezawodno\u015b\u0107 27(1), 1\u201316 (2025). https:\/\/doi.org\/10.17531\/EIN\/192164","DOI":"10.17531\/EIN\/192164"},{"key":"29447_CR49","doi-asserted-by":"publisher","unstructured":"\u00d6z\u00fcpak, Y. Machine learning-based fault detection in transmission lines: A comparative study with random search optimization. Bull. Pol. Acad. Sci. Tech. Sci. 73 (2). https:\/\/doi.org\/10.24425\/BPASTS.2025.153229 (2025).","DOI":"10.24425\/BPASTS.2025.153229"},{"key":"29447_CR50","doi-asserted-by":"crossref","unstructured":"\u00d6z\u00fcpak, Y. & Mansurov, S. Dec., Optimizing electricity demand forecasting with a novel RNN-LSTM hybrid model. Energy Sour. Part B Econ. Plann. Policy, 20, 1. 10.1080\/15567249.2025.2531448 (2025).","DOI":"10.1080\/15567249.2025.2531448"},{"key":"29447_CR51","doi-asserted-by":"publisher","unstructured":"\u00f6z\u00fcpak, Y. Real-time detection of photovoltaic module faults using a hybrid machine learning model. Sol. Energy. 302, 114014. https:\/\/doi.org\/10.1016\/J.SOLENER.2025.114014 ( 2025).","DOI":"10.1016\/J.SOLENER.2025.114014"},{"key":"29447_CR52","doi-asserted-by":"publisher","unstructured":"\u00d6z\u00fcpak, Y., Alpsalaz, F. & Aslan, E. Air quality forecasting using machine learning: Comparative analysis and ensemble strategies for enhanced prediction. Water Air Soil. Pollut. 236 (7), 1\u201317. https:\/\/doi.org\/10.1007\/S11270-025-08122-8\/TABLES\/5 (2025).","DOI":"10.1007\/S11270-025-08122-8\/TABLES\/5"},{"key":"29447_CR53","doi-asserted-by":"publisher","unstructured":"Liu, Z. et al. Swin transformer: hierarchical vision transformer using shifted windows. Proc. IEEE Int. Conf. Comput. Vis. 9992\u201310002. https:\/\/doi.org\/10.1109\/ICCV48922.2021.00986 (2021).","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"29447_CR54","doi-asserted-by":"publisher","unstructured":"Poudel, S. & Lee, S. W. Deep multi-scale attentional features for medical image segmentation. Appl. Soft Comput. 109, 107445. https:\/\/doi.org\/10.1016\/J.ASOC.2021.107445 (Sep. 2021).","DOI":"10.1016\/J.ASOC.2021.107445"},{"key":"29447_CR55","unstructured":"Lian, D., Yu, Z., Sun, X. & Gao, S. AS-MLP: An axial shifted MLP architecture for vision. In  ICLR \u201310th International Conference on Learning Representations.   https:\/\/arxiv.org\/abs\/2107.08391v2. Accessed 08 Mar 2025 (2022)."},{"key":"29447_CR56","doi-asserted-by":"publisher","unstructured":"Demirba\u015f, A. A., \u00dczen, H. & F\u0131rat, H. Spatial-attention convmixer architecture for classification and detection of gastrointestinal diseases using the Kvasir dataset. Health Inf. Sci. Syst. 12 (1), 1\u201315. https:\/\/doi.org\/10.1007\/S13755-024-00290-X\/FIGURES\/6 (2024).","DOI":"10.1007\/S13755-024-00290-X\/FIGURES\/6"},{"key":"29447_CR57","doi-asserted-by":"publisher","unstructured":"Peng, S. et al. Machine learning\u2013assisted internal standard calibration label-free SERS strategy for colon cancer detection. Anal. Bioanal Chem. 415 (9), 1699\u20131707. https:\/\/doi.org\/10.1007\/S00216-023-04566-1\/TABLES\/2 (2023).","DOI":"10.1007\/S00216-023-04566-1\/TABLES\/2"},{"key":"29447_CR58","doi-asserted-by":"publisher","unstructured":"Zhang, R., Zheng, Y., Poon, C. C. Y., Shen, D. & Lau, J. Y. W. Polyp detection during colonoscopy using a regression-based convolutional neural network with a tracker. Pattern Recognit 83,  209\u2013219  (2018). https:\/\/doi.org\/10.1016\/J.PATCOG.2018.05.026","DOI":"10.1016\/J.PATCOG.2018.05.026"},{"key":"29447_CR59","doi-asserted-by":"publisher","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A. & Chen, L. C. MobileNetV2: Inverted residuals and linear bottlenecks. In  Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 4510\u20134520 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00474","DOI":"10.1109\/CVPR.2018.00474"},{"key":"29447_CR60","doi-asserted-by":"publisher","unstructured":"Mokoatle, M., Marivate, V., Mapiye, D., Bornman, R. & Hayes, V. M. A review and comparative study of cancer detection using machine learning: SBERT and SimCSE application. BMC Bioinform. 24 (1), 1\u201325. https:\/\/doi.org\/10.1186\/S12859-023-05235-X\/FIGURES\/9 (2023).","DOI":"10.1186\/S12859-023-05235-X\/FIGURES\/9"},{"key":"29447_CR61","doi-asserted-by":"publisher","unstructured":"Peng, Y. & Deng, H. Medical image fusion based on machine learning for health diagnosis and monitoring of colorectal cancer. BMC Med. Imaging. 24 (1), 1\u201311. https:\/\/doi.org\/10.1186\/S12880-024-01207-6\/FIGURES\/7 (2024).","DOI":"10.1186\/S12880-024-01207-6\/FIGURES\/7"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-29447-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-29447-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-29447-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T15:24:00Z","timestamp":1777130640000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-29447-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,28]]},"references-count":61,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["29447"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-29447-8","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,28]]},"assertion":[{"value":"28 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"45583"}}