Brain Tumor Detection Using Machine Learning and Deep Learning Techniques: A Comprehensive Review
TL;DR
An extensive overview of current deep learning and machine learning techniques for brain tumor detection from MRI images is provided in this review study, which compares advanced deep learning architectures like Convolutional Neural Networks, ResNet, U-Net, Vision Transformers, and hybrid models with conventional machine learning techniques like Support Vector Machine, Random Forest, and K-Nearest Neighbor.
Abstract
One of the most dangerous neurological conditions is brain tumors, and better patient survival and efficient treatment planning depend on an early and precise diagnosis. The most popular imaging method for identifying brain tumors is magnetic resonance imaging (MRI), which offers a thorough image of brain structures without subjecting patients to ionizing radiation. Automated brain tumor identification and categorization has been much more accurate and efficient in recent years because to Artificial Intelligence (AI), especially Machine Learning (ML) and Deep Learning (DL). An extensive overview of current deep learning and machine learning techniques for brain tumor detection from MRI images is provided in this review study. It looks at popular preprocessing strategies, feature extraction techniques, classification algorithms, publically accessible datasets, and assessment measures from current research. It looks at popular preprocessing strategies, feature extraction techniques, classification algorithms, publically accessible datasets, and assessment measures from current research. Additionally, the study compares advanced deep learning architectures like Convolutional Neural Networks (CNN), ResNet, U-Net, Vision Transformers (ViT), and hybrid models with conventional machine learning techniques like Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN). Additionally, the study highlights the development of this research field by discussing current research trends using demonstrative PRISMA technique, bibliometric analysis, keyword co-occurrence analysis, and dataset analysis. Additionally, the study compares advanced deep learning architectures like Convolutional Neural Networks (CNN), ResNet, U-Net, Vision Transformers (ViT), and hybrid models with conventional machine learning techniques like Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN). Additionally, the study highlights the development of this research field by discussing current research trends using demonstrative PRISMA technique, bibliometric analysis, keyword co-occurrence analysis, and dataset analysis.