{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T13:12:17Z","timestamp":1788786737741,"version":"build-2803163510"},"reference-count":33,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,6]],"date-time":"2026-09-06T00:00:00Z","timestamp":1788652800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"F\u0131rat University Scientific Research Projects Unit","award":["MMY.26.05"],"award-info":[{"award-number":["MMY.26.05"]}]},{"award":["MMY.26.05"],"award-info":[{"award-number":["MMY.26.05"]}],"id":[{"id":"https:\/\/ror.org\/05teb7b63","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biomimetics"],"abstract":"<jats:p>Stroke is one of the leading causes of mortality and long-term neurological disability worldwide, and early diagnosis through accurate disease classification directly affects treatment success. Rapid differentiation of hemorrhagic and ischemic stroke on computed tomography (CT) images, together with accurate determination of the acute and chronic phase in ischemic cases, is of critical importance in the clinical decision-making process. In this study, StrokeCT-2C5K a two-center dataset comprising 5000 cranial CT images, was assembled specifically for this work. The images were reviewed by radiology specialists and assigned to one of four diagnostic categories: normal, hemorrhagic stroke, acute ischemic stroke, or chronic ischemic stroke. A Squeeze-and-Excitation (SE-Attention) mechanism was then integrated into DenseNet-121, ResNet-50, and EfficientNet-B3. From a biomimetic perspective, this channel-recalibration process provides a functional analogy to biological selective attention by giving greater weight to informative responses while reducing the influence of less relevant ones. All models were trained under the same training, validation, and test protocol; the standard CNN architectures were compared with their SE-Attention-enhanced counterparts. The results showed that the SE-Attention mechanism enables more effective learning of lesion-specific discriminative features by adaptively recalibrating channel-wise information, yielding an average classification accuracy improvement of 0.93 percentage points across all three architectures. The most pronounced improvements were observed in distinguishing ischemic from hemorrhagic stroke, as well as in distinguishing acute from chronic ischemic stroke. These findings show that a selective-information-processing strategy functionally analogous to biological attention can improve multi-class stroke classification across different CNN backbones.<\/jats:p>","DOI":"10.3390\/biomimetics11090638","type":"journal-article","created":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T12:12:25Z","timestamp":1788783145000},"page":"638","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["StrokeCT-2C5K: A Two-Center Cranial CT Dataset for Four-Class Stroke Classification Using SE-Attention-Enhanced Deep Learning Models"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5997-2546","authenticated-orcid":false,"given":"Ahmet Bahad\u0131r","family":"Karl\u0131","sequence":"first","affiliation":[{"name":"Department of Strategic Information Management Systems, Faculty of Medicine, Dicle University, Diyarbakir 21200, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9219-2262","authenticated-orcid":false,"given":"Murat","family":"Ucan","sequence":"additional","affiliation":[{"name":"Department of Computer Technologies, Vocational School of Technical Sciences, Dicle University, Diyarbakir 21200, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9505-181X","authenticated-orcid":false,"given":"Buket","family":"Kaya","sequence":"additional","affiliation":[{"name":"Department of Electronics and Automation, Firat University, Elazig 23119, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1016\/S1474-4422(21)00252-0","article-title":"Global, Regional, and National Burden of Stroke and Its Risk Factors, 1990\u20132019: A Systematic Analysis for the Global Burden of Disease Study 2019","volume":"20","author":"Feigin","year":"2021","journal-title":"Lancet Neurol."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1007\/s10278-021-00457-y","article-title":"Rapid Assessment of Acute Ischemic Stroke by Computed Tomography Using Deep Convolutional Neural Networks","volume":"34","author":"Lo","year":"2021","journal-title":"J. Digit. Imaging"},{"key":"ref_4","first-page":"60","article-title":"Epidemiology and Risk Factors of Stroke","volume":"10","author":"Alharbi","year":"2019","journal-title":"Arch. Pharm. Pract."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1109\/TPAMI.2022.3152247","article-title":"A Survey on Vision Transformer","volume":"45","author":"Han","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chin, C.-L., Lin, B.-J., Wu, G.-R., Weng, T.-C., Yang, C.-S., Su, R.-C., and Pan, Y.-J. (2017). An Automated Early Ischemic Stroke Detection System Using CNN Deep Learning Algorithm. Proceedings of the 2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST), IEEE.","DOI":"10.1109\/ICAwST.2017.8256481"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"109711","DOI":"10.1016\/j.compbiomed.2025.109711","article-title":"A Novel Hybrid ViT-LSTM Model with Explainable AI for Brain Stroke Detection and Classification in CT Images: A Case Study of Rajshahi Region","volume":"186","author":"Hossain","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chen, Y.-T., Chen, Y.-L., Chen, Y.-Y., Huang, Y.-T., Wong, H.-F., Yan, J.-L., and Wang, J.-J. (2022). Deep Learning\u2013Based Brain Computed Tomography Image Classification with Hyperparameter Optimization through Transfer Learning for Stroke. Diagnostics, 12.","DOI":"10.3390\/diagnostics12040807"},{"key":"ref_9","first-page":"192","article-title":"Transfer Learning-Based Classification Comparison of Stroke","volume":"IDAP-2022","author":"Alhatemi","year":"2022","journal-title":"Comput. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1038\/s41551-018-0324-9","article-title":"An Explainable Deep-Learning Algorithm for the Detection of Acute Intracranial Haemorrhage from Small Datasets","volume":"3","author":"Lee","year":"2018","journal-title":"Nat. Biomed. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1609","DOI":"10.3174\/ajnr.A5742","article-title":"Hybrid 3D\/2D Convolutional Neural Network for Hemorrhage Evaluation on Head CT","volume":"39","author":"Chang","year":"2018","journal-title":"Am. J. Neuroradiol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1146\/annurev.neuro.30.051606.094256","article-title":"Fundamental Components of Attention","volume":"30","author":"Knudsen","year":"2007","journal-title":"Annu. Rev. Neurosci."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018). Squeeze-and-Excitation Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6060","DOI":"10.1002\/mp.15122","article-title":"Comparison of Convolutional Neural Networks for Detecting Large Vessel Occlusion on Computed Tomography Angiography","volume":"48","author":"Remedios","year":"2021","journal-title":"Med. Phys."},{"key":"ref_15","unstructured":"Tan, M., and Le, Q.V. (2019, January 9\u201315). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the 36th International Conference on Machine Learning, ICML 2019, Long Beach, CA, USA."},{"key":"ref_16","first-page":"3581","article-title":"CNN Ensemble Approach to Detect COVID-19 from Computed Tomography Chest Images","volume":"67","author":"Alhichri","year":"2021","journal-title":"Comput. Mater. Contin."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e2968","DOI":"10.7717\/peerj-cs.2968","article-title":"Comparison of EfficientNet CNN Models for Multi-Label Chest X-Ray Disease Diagnosis","volume":"11","author":"Ucan","year":"2025","journal-title":"PeerJ Comput. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ucan, S., Ucan, M., and Kaya, M. (2023). Deep Learning Based Approach with EfficientNet and SE Block Attention Mechanism for Multiclass Alzheimer\u2019s Disease Detection. Proceedings of the 2023 4th International Conference on Data Analytics for Business and Industry (ICDABI), IEEE.","DOI":"10.1109\/ICDABI60145.2023.10629380"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020). ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"101908","DOI":"10.1016\/j.compmedimag.2021.101908","article-title":"Hemorrhagic Stroke Lesion Segmentation Using a 3D U-Net with Squeeze-and-Excitation Blocks","volume":"90","author":"Abramova","year":"2021","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"103720","DOI":"10.1016\/j.bspc.2022.103720","article-title":"Stroke Classification from Computed Tomography Scans Using 3D Convolutional Neural Network","volume":"76","author":"Neethi","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Umapathy, S., Murugappan, M., Bharathi, D., and Thakur, M. (2023). Automated Computer-Aided Detection and Classification of Intracranial Hemorrhage Using Ensemble Deep Learning Techniques. Diagnostics, 13.","DOI":"10.3390\/diagnostics13182987"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"24941","DOI":"10.1109\/JSEN.2020.3032897","article-title":"Deep Learning-Enhanced Internet of Medical Things to Analyze Brain CT Scans of Hemorrhagic Stroke Patients: A New Approach","volume":"21","author":"Xu","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"112109","DOI":"10.1016\/j.ejrad.2025.112109","article-title":"Deep Learning-Driven Multi-Class Classification of Brain Strokes Using Computed Tomography: A Step towards Enhanced Diagnostic Precision","volume":"187","author":"Kulathilake","year":"2025","journal-title":"Eur. J. Radiol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ozaltin, O., Coskun, O., Yeniay, O., and Subasi, A. (2022). A Deep Learning Approach for Detecting Stroke from Brain CT Images Using OzNet. Bioengineering, 9.","DOI":"10.3390\/bioengineering9120783"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Abdi, H., Sattar, M.U., Hasan, R., Dattana, V., and Mahmood, S. (2025). Stroke Detection in Brain CT Images Using Convolutional Neural Networks: Model Development, Optimization and Interpretability. Information, 16.","DOI":"10.3390\/info16050345"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"122559","DOI":"10.1016\/j.eswa.2023.122559","article-title":"Enhanced Non-Contrast Computed Tomography Images for Early Acute Stroke Detection Using Machine Learning Approach","volume":"240","author":"UmaMaheswaran","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"41936","DOI":"10.1038\/s41598-025-25857-w","article-title":"MedNet: A Lightweight Attention-Augmented CNN for Medical Image Classification","volume":"15","author":"Ferdous","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"126577","DOI":"10.1016\/j.neucom.2023.126577","article-title":"Class Attention to Regions of Lesion for Imbalanced Medical Image Recognition","volume":"555","author":"Zhuang","year":"2023","journal-title":"Neurocomputing"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ullah, Z., Hong, M., Mahmood, T., and Kim, J. (2025). Systematic Integration of Attention Modules into CNNs for Accurate and Generalizable Medical Image Classification. Mathematics, 13.","DOI":"10.3390\/math13223728"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018). Cbam: Convolutional Block Attention Module. Proceedings of the Proceedings of the European Conference on Computer Vision (ECCV), Springer.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Rastogi, D., Johri, P., Donelli, M., Agarwal, T., Tiwari, S., and Singh, P. (2025). XAI-BT-EdgeNet: Explainable Edge-Aware Deep Learning with Squeeze-and-Excitation for Brain Tumor Detection and Prediction. Front. Artif. Intell., 8.","DOI":"10.3389\/frai.2025.1676524"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1136\/svn-2024-003372","article-title":"Development of a Deep Learning Method to Identify Acute Ischaemic Stroke Lesions on Brain CT","volume":"10","author":"Fontanella","year":"2025","journal-title":"Stroke Vasc. Neurol."}],"container-title":["Biomimetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-7673\/11\/9\/638\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T12:55:58Z","timestamp":1788785758000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-7673\/11\/9\/638"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,6]]},"references-count":33,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["biomimetics11090638"],"URL":"https:\/\/doi.org\/10.3390\/biomimetics11090638","relation":{},"ISSN":["2313-7673"],"issn-type":[{"value":"2313-7673","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,6]]}}}