Brain tumor grading based on Neural Networks and Convolutional Neural Networks

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Oncology

Background:

• Accurate brain tumor grading is crucial for effective treatment planning.
• Traditional methods often rely on manual feature extraction, which can be labor-intensive and subjective.
• Deep learning offers a promising avenue for automated and objective tumor grading.

Purpose of the Study:

• To evaluate the efficacy of deep learning models, specifically Convolutional Neural Networks (CNNs), for brain tumor grading using multiphase MRI data.
• To compare the performance of CNNs against baseline Neural Networks (NNs) in terms of grading accuracy.
• To explore the feature learning capabilities of CNNs in the context of medical image analysis.

Main Methods:

• Multiphase Magnetic Resonance Imaging (MRI) datasets were utilized directly as input for deep learning models.
• Various configurations of CNN architectures were investigated.
• Performance was assessed using sensitivity and specificity metrics on a testing dataset.
• Kernel visualization and self-learned feature display were employed to understand CNN behavior.

Main Results:

• CNNs demonstrated a significant improvement in brain tumor grading performance, with a maximum increase of 18% in sensitivity and specificity compared to NNs.
• The proposed approach bypasses the need for manual feature engineering by leveraging the inherent feature learning capabilities of deep learning.
• Visualizations provided insights into the features learned by different CNN layers.

Conclusions:

• Deep learning, particularly CNNs, offers a powerful and automated approach for brain tumor grading from multiphase MRI.
• CNNs outperform traditional NNs in this task, leading to more accurate and objective grading.
• The ability of CNNs to learn relevant features directly from image data simplifies the analysis pipeline and enhances diagnostic potential.