{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T21:05:51Z","timestamp":1765487151978,"version":"3.41.2"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T00:00:00Z","timestamp":1729641600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neuroinform."],"abstract":"<jats:p>In recent years, intracerebral hemorrhage (ICH) has garnered significant attention as a severe cerebrovascular disorder. To enhance the accuracy of ICH detection and segmentation, this study proposed an improved fuzzy C-means (FCM) algorithm and performed a comparative analysis with both traditional FCM and advanced convolutional neural network (CNN) algorithms. Experiments conducted on the publicly available CT-ICH dataset evaluated the performance of these three algorithms in predicting ICH volume. The results demonstrated that the improved FCM algorithm offered notable improvements in computational time and resource consumption compared to the traditional FCM algorithm, while also showing enhanced accuracy. However, it still lagged behind the CNN algorithm in areas such as feature extraction, model generalization, and the ability to handle complex image structures. The study concluded with a discussion of potential directions for further optimizing the FCM algorithm, aiming to bridge the performance gap with CNN algorithms and provide a reference for future research in medical image processing.<\/jats:p>","DOI":"10.3389\/fninf.2024.1440304","type":"journal-article","created":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T04:58:37Z","timestamp":1729659517000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Fuzzy C-means clustering algorithm applied in computed tomography images of patients with intracranial hemorrhage"],"prefix":"10.3389","volume":"18","author":[{"given":"Lintao","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dewen","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huiying","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Ye","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zengliang","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2024,10,23]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"775","DOI":"10.3390\/diagnostics13040775","article-title":"IoT-enabled classification of echocardiogram images for cardiovascular disease risk prediction with pre-trained recurrent convolutional neural networks","volume":"13","author":"Balakrishnan","year":"2023","journal-title":"Diagnostics (Basel)"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"e0154690","DOI":"10.1371\/journal.pone.0154690","article-title":"Quantitative real-time polymerase chain reaction measurement of HLA-DRA gene expression in whole blood is highly reproducible and shows changes that reflect dynamic shifts in monocyte surface HLA-DR expression during the course of sepsis","volume":"11","author":"Cajander","year":"2016","journal-title":"PLoS One"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"100712","DOI":"10.1016\/j.bonr.2020.100712","article-title":"Severe osteomalacia with multiple insufficiency fractures secondary to intravenous iron therapy in a patient with Rendu-Osler-weber syndrome","volume":"13","author":"Callejas-Moraga","year":"2020","journal-title":"Bone Rep"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"531","DOI":"10.21037\/atm-20-7588","article-title":"Gradient nanostructured titanium stimulates cell responses in vitro and enhances osseointegration in vivo","volume":"9","author":"Cao","year":"2021","journal-title":"Ann Transl Med"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"104893","DOI":"10.1016\/j.envint.2019.06.003","article-title":"Comparing flow cytometry with culture-based methods for microbial monitoring and as a diagnostic tool for assessing drinking water treatment processes","volume":"130","author":"Cheswick","year":"2019","journal-title":"Environ. 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Neurosurg."},{"key":"ref18","doi-asserted-by":"publisher","first-page":"3499","DOI":"10.1016\/j.radcr.2021.08.038","article-title":"Early rebleeding of a foramen magnum dural arteriovenous fistula: a case report and review of the literature","volume":"16","author":"Okamoto","year":"2021","journal-title":"Radiol Case Rep"},{"key":"ref19","first-page":"1037","article-title":"Spontaneous acute bleeding within subdural effusion from dural metastasis of gastric cancer: a case report","volume":"19","author":"Ortega Rodriguez","year":"2021","journal-title":"Neurocirugia (Astur: Engl Ed)"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"2237","DOI":"10.1182\/bloodadvances.2020002398","article-title":"Systematic review and meta-analysis of outcomes in patients with suspected pulmonary embolism","volume":"5","author":"Patel","year":"2021","journal-title":"Blood Adv."},{"key":"ref21","doi-asserted-by":"publisher","first-page":"e13587","DOI":"10.7759\/cureus.13587","article-title":"Traumatic brain injury in the elderly: clinical features, prognostic factors, and outcomes of 133 consecutive surgical patients","volume":"13","author":"Podolsky-Gondim","year":"2021","journal-title":"Cureus"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1007\/s11239-021-02491-7","article-title":"Impact of a pharmacist driven anticoagulation reversal program at a large academic medical center","volume":"53","author":"Procopio","year":"2022","journal-title":"J. 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