Deep Learning for Smart Healthcare-A Survey on Brain Tumor Detection from Medical Imaging

Area of Science:

• Medical imaging
• Artificial intelligence
• Neuro-oncology

Background:

• Brain tumors represent a significant global health challenge, necessitating advanced diagnostic tools.
• Magnetic resonance imaging (MRI) is a primary modality for brain tumor diagnosis and analysis.
• Technological advancements, especially in artificial intelligence (AI), offer new avenues for improving medical diagnostics.

Purpose of the Study:

• To conduct a comprehensive review of deep learning applications in brain tumor detection using MRI data.
• To identify current challenges and explore future research directions in this domain.
• To specifically examine the role of convolutional neural networks (CNNs) in processing brain MRI images.

Main Methods:

• Systematic review of existing literature on deep learning techniques applied to brain MRI.
• Analysis of various CNN architectures and their performance in medical image processing.
• Identification of common challenges and limitations in current AI-driven brain tumor diagnosis.

Main Results:

• Deep learning, especially CNNs, has demonstrated significant success in analyzing complex medical images like brain MRIs.
• Various CNN architectures have been explored for brain tumor segmentation and classification.
• Key challenges include data variability, interpretability, and clinical integration of AI models.

Conclusions:

• AI, particularly CNNs, holds substantial potential to enhance the accuracy and efficiency of brain tumor diagnosis via MRI.
• Further research is needed to address existing challenges and facilitate the clinical translation of these technologies.
• Future directions include developing more robust, interpretable, and integrated AI solutions for neuro-oncology.