{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:15:27Z","timestamp":1777706127851,"version":"3.51.4"},"reference-count":29,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2022,3,10]],"date-time":"2022-03-10T00:00:00Z","timestamp":1646870400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2022,6,9]]},"abstract":"<jats:p>Researchers used visual methods rigorously to improve brain tumor detection in MRI or CT scans, yet there remains a challenge to improve the detection accuracy. Further, the rise of deep learning methods improved tumor detection accuracy up to the mark. But again, many times, we face the challenges of having a bigger dataset and better computing power to achieve an improved and accurate trained model for every object classification problem.<\/jats:p>\n                  <jats:p>In this paper, we propose a deep learning framework single shot multi-box detector (SSD)-based model to detect tumors in the MRI scans. The proposed SSD model is the faster algorithm to detect the tumor even with the ability to detect the smallest spot in the low-resolution MRI scans. We additionally used a lightweight neural network architecture MobileNet v2 with SSD for faster and accurate object classification. The experimental results showed 98% accuracy with the proposed method after training with the smallest dataset of 250 MRI scans. We used the Kaggle database for training and testing the proposed model.<\/jats:p>","DOI":"10.3233\/jifs-219298","type":"journal-article","created":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T11:58:33Z","timestamp":1646999913000},"page":"1985-1993","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Brain tumor detection in MRI scans using single shot multibox detector"],"prefix":"10.1177","volume":"43","author":[{"family":"Naseer-u-Din","sequence":"first","affiliation":[{"name":"University of Balochistan, Quetta, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdul","family":"Basit","sequence":"additional","affiliation":[{"name":"University of Balochistan, Quetta, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ihsan","family":"Ullah","sequence":"additional","affiliation":[{"name":"University of Balochistan, Quetta, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Waheed","family":"Noor","sequence":"additional","affiliation":[{"name":"University of Balochistan, Quetta, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Atiq","family":"Ahmed","sequence":"additional","affiliation":[{"name":"University of Balochistan, Quetta, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naveed","family":"Sheikh","sequence":"additional","affiliation":[{"name":"University of Balochistan, Quetta, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2022,3,10]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Brain MRI Images for Brain Tumor Detection. https:\/\/www.kaggle.com\/navoneel\/brain-mri-imagesfor-brain-tumor-detection. 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In 14th International Conference on Mathematical Methods and Computational Techniques in Electrical Engineering (MMACTEE13) 2012."},{"issue":"1","key":"e_1_3_2_5_2","first-page":"12","article-title":"Brain tumorextraction in mri images using clustering and morphologicaloperations techniques","volume":"4","author":"\u00a0Ali S.M.","year":"2013","unstructured":"\u00a0AliS.M., Loay\u00a0Kadom Abood and Rab5ab\u00a0Saadoon Abdoon, Brain tumorextraction in mri images using clustering and morphologicaloperations techniques, International Journal of GeographicalInformation System Applications and Remote Sensing 4(1) (2013), 12\u201325.","journal-title":"International Journal of GeographicalInformation System Applications and Remote Sensing"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbe.2018.10.004"},{"key":"e_1_3_2_7_2","doi-asserted-by":"crossref","unstructured":"Nilesh\u00a0Bhaskarrao Bahadure Arun\u00a0Kumar Ray and Har\u00a0Pal Thethi Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM. 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IEEE 2012."},{"key":"e_1_3_2_18_2","unstructured":"Madhumantee Naskar An automated system for brain tumor detection & segmentation Madhumantee Naskar Journal of Engineering Research and Studies 2015."},{"key":"e_1_3_2_19_2","unstructured":"Sadaf Naz and Nitesh Kumar An Efficient Brain Tumor Detection system using Automatic segmentation with Convolution Neural Network. 2019."},{"key":"e_1_3_2_20_2","doi-asserted-by":"crossref","unstructured":"Ioan P\u0103v\u0103loi and Anca Ignat Experiments on iris recognition using partially occluded images. In International Workshop Soft Computing Applications pages 153\u2013173. 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