A systematic review on deep learning based brain tumor segmentation and detection using MRI: Past insights, present...

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.