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The existing approaches are time-consuming, incursive, and susceptible to human mistake. These drawbacks highlight the importance of developing a completely automated deep learning-based approach for segmentation and classification of brain tumors. The expedient and prompt segmentation and classification of a brain tumor are critical for accurate clinical diagnosis and adequately treatment. As a result, deep learning-based brain tumor segmentation and classification algorithms are extensively employed. In the deep learning-based brain tumor segmentation and classification technique, the CNN model has an excellent brain segmentation and classification effect. In this work, an integrated and hybrid approach based on deep convolutional neural network and machine learning classifiers is proposed for the accurate segmentation and classification of brain MRI tumor. A CNN is proposed in the first stage to learn the feature map from image space of brain MRI into the tumor marker region. In the second step, a faster region-based CNN is developed for the localization of tumor region followed by region proposal network (RPN). In the last step, a deep convolutional neural network and machine learning classifiers are incorporated in series in order to further refine the segmentation and classification process to obtain more accurate results and findings. The proposed model\u2019s performance is assessed based on evaluation metrics extensively used in medical image processing. The experimental results validate that the proposed deep CNN and SVM-RBF classifier achieved an accuracy of 98.3% and a dice similarity coefficient (DSC) of 97.8% on the task of classifying brain tumors as gliomas, meningioma, or pituitary using brain dataset-1, while on Figshare dataset, it achieved an accuracy of 98.0% and a DSC of 97.1% on classifying brain tumors as gliomas, meningioma, or pituitary. The segmentation and classification results demonstrate that the proposed model outperforms state-of-the-art techniques by a significant margin.<\/jats:p>","DOI":"10.1155\/2022\/6446680","type":"journal-article","created":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T02:05:28Z","timestamp":1660010728000},"page":"1-18","source":"Crossref","is-referenced-by-count":63,"title":["A Hybrid Approach Based on Deep CNN and Machine Learning Classifiers for the Tumor Segmentation and Classification in Brain MRI"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0099-0708","authenticated-orcid":true,"given":"Ejaz Ul","family":"Haq","sequence":"first","affiliation":[{"name":"Guangdong Key Laboratory of Intelligent Information Processing, School of Electronics and Information Engineering, Shenzhen University, China"},{"name":"School of Computer and Information Engineering, Xiamen University of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7040-3591","authenticated-orcid":true,"given":"Huang","family":"Jianjun","sequence":"additional","affiliation":[{"name":"Guangdong Key Laboratory of Intelligent Information Processing, School of Electronics and Information Engineering, Shenzhen University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6955-7072","authenticated-orcid":true,"given":"Xu","family":"Huarong","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Xiamen University of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kang","family":"Li","sequence":"additional","affiliation":[{"name":"Guangdong Key Laboratory of Intelligent Information Processing, School of Electronics and Information Engineering, Shenzhen University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lifen","family":"Weng","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Xiamen University of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbe.2018.10.004"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.26599\/BDMA.2018.9020001"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1016\/j.mlwa.2020.100003"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1007\/s00062-020-00884-4"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2017.07.007"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2535302"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-018-5595-8"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-5788-0_38"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1172\/1\/012016"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1109\/ECACE.2019.8679240"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1080\/1931308x.2007.10644134"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCCEEE.2018.8515763"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocs.2018.12.003"},{"key":"14","doi-asserted-by":"crossref","first-page":"1822","DOI":"10.1007\/978-3-030-31635-8_221","article-title":"Distinguishing functional from non-functional pituitary macroadenomas with a machine learning analysis","volume-title":"Mediterranean Conference on Medical and Biological Engineering and Computing","author":"R. 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