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Death rates have been rising annually for the past few decades due to negligence of early treatment of brain tumors. To reduce the death rate, early identification of tumors is crucial. Early brain tumor detection may potentially lower the risk of life. Manual tumor diagnosis is complex, challenging, and time\u2010consuming for medical professionals. Therefore, automatic detection and segmentation methods simplify the diagnosing procedure. Thus, automatic segmentation and classification methods are taken up to make the diagnosis process easy. This research proposes a two\u2010dimensional cumulative sum average filter (2D\u2010CSAF) for preprocessing images and an improved deviation sparse fuzzy C\u2010means (IDSFCM) with neighbor information for segmenting brain tumors from magnetic resonance images. The novel IDSFCM segmentation increases the noise reduction capability and enhances segmentation accuracies. The hybrid modified sine cosine algorithm\u2010crow search algorithm (MSCA\u2010CSA)\u2013based WELM model is proposed to classify the brain tumor. The MSCA\u2010CSA algorithm optimizes the weights of the WELM model to increase the classification capability. The gray level co\u2010occurrence matrix (GLCM) feature extraction technique is employed to extract the features from the segmented images, and extracted features are given as input to the MSCA\u2010CSA\u2010WELM model for classification. The brain tumor dataset from Harvard Medical School is considered for this research. The proposed IDSFCM segmentation achieved 99.53% segmentation accuracy. The accuracy, specificity, and sensitivity performance measures are considered for the classification. The classification performance was evaluated using accuracy, sensitivity, and specificity metrics. The proposed MSCA\u2010CSA\u2013based WELM model outperformed feature extraction\u2013based classifiers, achieving 99.37% accuracy, 99.87% sensitivity, and 99.44% specificity during training.<\/jats:p>","DOI":"10.1155\/acis\/9991264","type":"journal-article","created":{"date-parts":[[2025,6,11]],"date-time":"2025-06-11T05:05:17Z","timestamp":1749618317000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improved Deviation Sparse Fuzzy C\u2010Means\u20102D Cumulative Sum Average Filter and Modified Sine Cosine Crow Search Algorithm\u2010Wavelet Extreme Learning Machine for Brain Tumor Detection and Classification"],"prefix":"10.1155","volume":"2025","author":[{"given":"Suvashisa","family":"Dash","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3515-4467","authenticated-orcid":false,"given":"Satyasis","family":"Mishra","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8092-2050","authenticated-orcid":false,"given":"Mohammed","family":"Siddique","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7948-8285","authenticated-orcid":false,"given":"Demissie J.","family":"Gelmecha","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7952-4736","authenticated-orcid":false,"given":"Ram Sewak","family":"Singh","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2025,6,11]]},"reference":[{"key":"e_1_2_12_1_2","doi-asserted-by":"publisher","DOI":"10.1093\/neuonc\/noac202"},{"key":"e_1_2_12_2_2","unstructured":"American Cancer Society\u2019s Publication Cancer Facts & Figures 2022; the ACS Website; the CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2014-2018 2021 https:\/\/www.cancer.net\/cancer-types\/brain-tumor\/statistics."},{"key":"e_1_2_12_3_2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21763"},{"key":"e_1_2_12_4_2","unstructured":"American Cancer Society https:\/\/cancerstatisticscenter.cancer.org\/types\/brain-and-other-nervous-system."},{"key":"e_1_2_12_5_2","doi-asserted-by":"publisher","DOI":"10.1080\/02533839.2021.1919561"},{"key":"e_1_2_12_6_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0295951"},{"key":"e_1_2_12_7_2","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2024.1377285"},{"key":"e_1_2_12_8_2","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbad481"},{"key":"e_1_2_12_9_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-61322-w"},{"key":"e_1_2_12_10_2","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2023.037857"},{"key":"e_1_2_12_11_2","doi-asserted-by":"publisher","DOI":"10.3390\/curroncol30030203"},{"key":"e_1_2_12_12_2","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering11030266"},{"key":"e_1_2_12_13_2","doi-asserted-by":"publisher","DOI":"10.1080\/02533839.2021.2012525"},{"key":"e_1_2_12_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.01.106"},{"key":"e_1_2_12_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/42.996338"},{"key":"e_1_2_12_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/tsmcb.2004.831165"},{"key":"e_1_2_12_17_2","unstructured":"SzilagyiL. 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