{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T22:56:42Z","timestamp":1780441002881,"version":"3.54.1"},"reference-count":19,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,14]],"date-time":"2025-02-14T00:00:00Z","timestamp":1739491200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MTI"],"abstract":"<jats:p>[Background] In computed tomography (CT) for intracranial hemorrhage (ICH), the various window settings and the continuity of slices are critical factors for accurate diagnosis. However, traditional convolutional neural networks typically accept only single-slice images. Since ICH lesions often extend across multiple slices, using only single-slice images may result in reduced diagnostic accuracy by neglecting spatial continuity. Our approach addresses this limitation by integrating multi-slice information through a 9-channel pseudo-color map. To address this limitation, we explored the use of a 9-channel pseudo-color map for the discrimination of ICH in CT. [Method] A total of 21,744 cases (normal controls: 12,862; abnormal cases: 8882) from an open dataset were utilized for model training and validation. Abnormal cases included a variety of ICHs. The 9-channel pseudo-color map was generated by combining three different window settings with three continuous slices. ResNeXt50-32x4d architecture with five-fold cross-validation used. A total of 956 clinical cases were used for model testing. [Result] A total of 558,738 images were included in the model training process. The optimal model performance metrics were as follows: accuracy: 95.92%, sensitivity: 96.37%, and specificity: 95.24%. The average processing time for each case was recorded as 3.29 s. [Conclusions] The 9-channel pseudo-color map demonstrates high accuracy in the discrimination of ICH in CT images using deep learning methodologies.<\/jats:p>","DOI":"10.3390\/mti9020017","type":"journal-article","created":{"date-parts":[[2025,2,14]],"date-time":"2025-02-14T11:14:41Z","timestamp":1739531681000},"page":"17","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Application of 9-Channel Pseudo-Color Maps in Deep Learning for Intracranial Hemorrhage Detection"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5881-1274","authenticated-orcid":false,"given":"Shimpei","family":"Sato","sequence":"first","affiliation":[{"name":"Department of Radiology, Otaru General Hospital, Otaru 047-0152, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3143-7577","authenticated-orcid":false,"given":"Daisuke","family":"Oura","sequence":"additional","affiliation":[{"name":"Department of Radiology, Otaru General Hospital, Otaru 047-0152, Japan"},{"name":"Department of Biomedical Science and Engineering, Faculty of Health Sciences, Hokkaido University, Sapporo 060-0812, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7796-5113","authenticated-orcid":false,"given":"Hiroyuki","family":"Sugimori","sequence":"additional","affiliation":[{"name":"Department of Biomedical Science and Engineering, Faculty of Health Sciences, Hokkaido University, Sapporo 060-0812, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1177\/17474930211065917","article-title":"World Stroke Organization (WSO): Global Stroke Fact Sheet 2022","volume":"17","author":"Feigin","year":"2022","journal-title":"Int. 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