A systematic review on deep learning based brain tumor segmentation and detection using MRI: Past insights, present techniques and future trends
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View abstract on PubMed
Summary
This summary is machine-generated.Early detection of brain tumors using Magnetic Resonance Imaging (MRI) and deep learning models significantly improves patient survival. This review analyzes deep learning techniques for robust brain tumor segmentation and detection from MRI scans.
Area Of Science
- Medical Imaging
- Artificial Intelligence
- Oncology
Background
- Brain tumors are a leading cause of adult mortality worldwide.
- Early diagnosis of brain tumors is critical for improving patient survival rates and therapeutic outcomes.
- Magnetic Resonance Imaging (MRI) offers comprehensive data for brain tumor detection and differentiation.
Purpose Of The Study
- To provide a comprehensive overview of brain tumor segmentation and detection techniques.
- To analyze the application of deep learning models in processing large volumes of MRI data for brain tumor analysis.
- To highlight current challenges and research gaps in the field.
Main Methods
- Review of deep learning-based models for brain tumor recognition and segmentation.
- Chronological analysis to validate the robustness of various techniques.
- Discussion of dataset details, performance evaluations, and simulation tools.
Main Results
- Deep learning models demonstrate effectiveness in analyzing MRI data for brain tumor detection.
- Strengths and limitations of standard deep learning methods are discussed.
- Identified key challenges and research gaps in current models.
Conclusions
- Deep learning offers a powerful approach for brain tumor segmentation and detection using MRI.
- Further research is needed to address existing challenges and improve model performance.
- Optimized models can enhance early diagnosis and patient outcomes for brain malignancies.
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