Exploring the State-of-the-Art Algorithms for Brain Tumor Classification Using MRI Data
Brain structural magnetic resonance image (MRI) is a very useful way to learn about the structure and function of the brain. For doctors, finding and classifying brain tumor (BT) is a routine and difficult procedure. Digital image processing methods such as segmentation, classification, and pre-proc...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11036710/ |
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| Summary: | Brain structural magnetic resonance image (MRI) is a very useful way to learn about the structure and function of the brain. For doctors, finding and classifying brain tumor (BT) is a routine and difficult procedure. Digital image processing methods such as segmentation, classification, and pre-processing may assist medical practitioners in diagnosing certain forms of brain cancer, finding tumors, and observing small changes. In this paper, cutting-edge machine learning (ML) and deep learning (DL) techniques are used to show how BT is currently being diagnosed with MRI images. This study is about the algorithms, datasets, and model designs that different researchers have utilized for classifying BT images. The study also looks at how well AI algorithms work now, what problems they face, and what possible research paths they could follow in the future. This study aims to keep experts up to date on the latest developments in this challenging area and get them interested in it again. BT classification using MR scans may be accomplished using computer-aided design (CAD) systems by using digital image processing techniques including segmentation, classification, and pre-processing. In this paper, we discuss the traditional ML and DL methods for detecting BT. This study takes a look at the present state of the art in methodology and may be used to create plans for successful diagnostics of additional brain diseases employing different MRI techniques. |
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| ISSN: | 2169-3536 |