{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T04:38:40Z","timestamp":1775018320100,"version":"3.50.1"},"reference-count":26,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T00:00:00Z","timestamp":1745452800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Stroke detection using medical imaging plays a crucial role in early diagnosis and treatment planning. In this study, we propose a Convolutional Neural Network (CNN)-based model for detecting strokes from brain Computed Tomography (CT) images. The model is trained on a dataset consisting of 2501 images, including both normal and stroke cases, and employs a series of preprocessing steps, including resizing, normalization, data augmentation, and splitting into training, validation, and test sets. The CNN architecture comprises three convolutional blocks followed by dense layers optimized through hyperparameter tuning to maximize performance. Our model achieved a validation accuracy of 97.2%, with precision and recall values of 96%, demonstrating high efficacy in stroke classification. Additionally, interpretability techniques such as Local Interpretable Model-agnostic Explanations (LIME), occlusion sensitivity, and saliency maps were used to visualize the model\u2019s decision-making process, enhancing transparency and trust for clinical use. The results suggest that deep learning models, particularly CNNs, can provide valuable support for medical professionals in detecting strokes, offering both high performance and interpretability. The model demonstrates moderate generalizability, achieving 89.73% accuracy on an external, patient-independent dataset of 9900 CT images, underscoring the need for further optimization in diverse clinical settings.<\/jats:p>","DOI":"10.3390\/info16050345","type":"journal-article","created":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T11:44:52Z","timestamp":1745495092000},"page":"345","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Stroke Detection in Brain CT Images Using Convolutional Neural Networks: Model Development, Optimization and Interpretability"],"prefix":"10.3390","volume":"16","author":[{"given":"Hassan","family":"Abdi","sequence":"first","affiliation":[{"name":"College of Science and Engineering, University of Derby, Kedleston Road, Derby DE22 1GB, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1457-3021","authenticated-orcid":false,"given":"Mian Usman","family":"Sattar","sequence":"additional","affiliation":[{"name":"College of Science and Engineering, University of Derby, Kedleston Road, Derby DE22 1GB, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8089-837X","authenticated-orcid":false,"given":"Raza","family":"Hasan","sequence":"additional","affiliation":[{"name":"Department of Science and Engineering, Solent University, Southampton SO14 0YN, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0661-1174","authenticated-orcid":false,"given":"Vishal","family":"Dattana","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Management Information System, Oman College of Management & Technology, P.O. Box 680, Barka 320, Oman"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2860-4095","authenticated-orcid":false,"given":"Salman","family":"Mahmood","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Nazeer Hussain University, ST-2, Near Karimabad, Karachi 75950, Pakistan"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mainali, S., Darsie, M.E., and Smetana, K.S. (2021). Machine Learning in Action: Stroke Diagnosis and Outcome Prediction. Front. Neurol., 12.","DOI":"10.3389\/fneur.2021.734345"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"750","DOI":"10.3174\/ajnr.A7872","article-title":"Advances in Acute Ischemic Stroke Treatment: Current Status and Future Directions","volume":"44","author":"Bathla","year":"2023","journal-title":"Am. J. Neuroradiol. 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