Development of a Model for Detection of Brain Tumor Using Deep Learning

Brain tumor occurs owing to uncontrolled and rapid growth of cells in the Brain. If not treated at an initial phase, it may lead to death. Despite many significant efforts and promising outcomes in this domain, accurate segmentation and classification remain a challenging task. A major challenge for brain tumor detection arises from the variations in tumor location, shape, and size. The objective of this survey is to deliver a comprehensive literature on brain tumor detection through magnetic resonance imaging to help the researchers. This survey covered the anatomy of brain tumors, publicly available datasets, enhancement techniques, segmentation, feature extraction, classification, and deep learning, transfer learning and quantum machine learning for brain tumors analysis. Finally, this survey provides all important literature for the detection of brain tumors with their advantages, limitations, developments, and future trends.

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Published
2024-11-26
DOI
https://doi.org/10.1109/nigercon62786.2024.10927075
Primary Topic
Brain Tumor Detection and Classification
Type
article
Field-Weighted Citation Impact
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article

Development of a Model for Detection of Brain Tumor Using Deep Learning

Adebusola M. Tope-Oke, G.O. Babalola, Opani M. Aweh, Abiodun Oguntimilehin et al.
Brain Tumor Detection and Classification
article

Development of a Model for Detection of Brain Tumor Using Deep Learning

Adebusola M. Tope-Oke, G.O. Babalola, Opani M. Aweh, Abiodun Oguntimilehin, Okebule Toyin, Oluwatoyin Bunmi Abiola
article en

Abstract

Brain tumor occurs owing to uncontrolled and rapid growth of cells in the Brain. If not treated at an initial phase, it may lead to death. Despite many significant efforts and promising outcomes in this domain, accurate segmentation and classification remain a challenging task. A major challenge for brain tumor detection arises from the variations in tumor location, shape, and size. The objective of this survey is to deliver a comprehensive literature on brain tumor detection through magnetic resonance imaging to help the researchers. This survey covered the anatomy of brain tumors, publicly available datasets, enhancement techniques, segmentation, feature extraction, classification, and deep learning, transfer learning and quantum machine learning for brain tumors analysis. Finally, this survey provides all important literature for the detection of brain tumors with their advantages, limitations, developments, and future trends.

Afe Babalola University (NG)
Openalex Percentile: Top 68%
Brain Tumor Detection and Classification
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Development of a Model for Detection of Brain Tumor Using Deep Learning — Adebusola M. Tope-Oke, G.O. Babalola, et al. · (2024) | TGRS Research Map | TGRS