An efficient ML approach to detect brain tumor using MRI images
| bracu.type.group | Research Publications | |
| datacite.rights | Open Access | |
| dc.contributor.author | Muktadir, Md. Arafat | |
| dc.contributor.author | Rahmat Ullah, A.S.M. | |
| dc.contributor.author | Islam, Md. Jubayer | |
| dc.contributor.author | Hossain, Emdad | |
| dc.contributor.author | Munny, Tanjila Akter | |
| dc.contributor.author | Muaz Ali, Syed | |
| dc.contributor.author | Alam, Md. Ashraful | |
| dc.contributor.department | Department of Computer Science and Engineering | |
| dc.date.accessioned | 2026-09-29T06:36:38Z | |
| dc.date.available | 2026-09-29T06:36:38Z | |
| dc.date.issued | 2023-01-01 | |
| dc.description.abstract | Brain tumors have become one of the most leading causes of death worldwide. Early diagnosis of brain tumor is really important as it spreads quickly. One of the most challenging task in medical field is tumor detection and classification. Medical data has revealed that manual tumor detection and classification with human assistance can result in an incorrect prediction and diagnosis. Recently, combining machine learning with deep learning algorithms have shown promising outcomes for enhancing the accuracy and efficiency of brain tumor detection and classification from MRI images. In our paper our main target is to find better accuracy as well as more efficient way to detect and classify brain tumor from the MRI images using Image Processing, Machine Learning and Deep Learning. | |
| dc.description.version | Published | |
| dc.format.extent | 6 Pages | |
| dc.identifier.citation | M. A. Muktadir et al., "An Efficient ML Approach to Detect Brain Tumor Using MRI Images," 2023 26th International Conference on Computer and Information Technology (ICCIT), Cox's Bazar, Bangladesh, 2023, pp. 1-6, doi: 10.1109/ICCIT60459.2023.10441645. | |
| dc.identifier.doi | 10.1109/ICCIT60459.2023.10441645 | |
| dc.identifier.issn | 9798350359015 | |
| dc.identifier.other | 2-s2.0-85187377837 | |
| dc.identifier.uri | https://hdl.handle.net/10361/30280 | |
| dc.language.iso | en_US | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.relation.hasversion | 10.1109/ICCIT60459.2023.10441645 | |
| dc.relation.ispartof | 2023 26th International Conference on Computer and Information Technology Iccit 2023 | |
| dc.relation.ispartofseries | 2023 26th International Conference on Computer and Information Technology Iccit 2023 | |
| dc.relation.uri | https://ieeexplore.ieee.org/document/10441645 | |
| dc.subject | Deep learning | |
| dc.subject | Training | |
| dc.subject | Machine learning algorithms | |
| dc.subject | Magnetic resonance imaging | |
| dc.subject | Feature extraction | |
| dc.subject | Medical diagnostic imaging | |
| dc.subject | Tumors | |
| dc.subject | Image processing | |
| dc.subject.lcsh | Brain--Tumors--Diagnosis. | |
| dc.subject.lcsh | Magnetic resonance imaging. | |
| dc.subject.lcsh | Deep learning (Machine learning). | |
| dc.title | An efficient ML approach to detect brain tumor using MRI images | |
| dc.type | Conference Proceeding | |
| person.affiliation.name | BRAC University | |
| person.affiliation.name | BRAC University | |
| person.affiliation.name | BRAC University | |
| person.affiliation.name | BRAC University | |
| person.affiliation.name | BRAC University | |
| person.affiliation.name | BRAC University | |
| person.affiliation.name | BRAC University | |
| person.identifier.scopus-author-id | 58930678100 | |
| person.identifier.scopus-author-id | 58930485500 | |
| person.identifier.scopus-author-id | 57207729147 | |
| person.identifier.scopus-author-id | 59026267400 | |
| person.identifier.scopus-author-id | 58931448600 | |
| person.identifier.scopus-author-id | 58144182000 | |
| person.identifier.scopus-author-id | 58813137600 |
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