Deep learning and SHAP analysis enhance MRI brain tumor detection

Researchers have developed an enhanced method for detecting brain tumors using Magnetic Resonance Imaging (MRI) combined with deep learning and explainable AI. The study explored various feature representation schemes, including Local Binary Patterns (LBP), Gabor filters, and Convolutional Neural Networks (CNN), alongside several learning algorithms like Support Vector Classifier (SVC) and CNN. Empirical results showed that LBP with SVC achieved 98.06% accuracy on a small dataset, while CNN reached 98.9% accuracy on a larger benchmark dataset, demonstrating high precision in automated tumor diagnosis. AI

IMPACT Enhances diagnostic accuracy for brain tumors, potentially leading to earlier and more effective treatment.

RANK_REASON The item is a research paper detailing a novel methodology for medical image analysis. [lever_c_demoted from research: ic=1 ai=1.0]

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• convolutional neural network
• discrete wavelet transform
• fast Fourier transform
• GitHub
• k-nearest neighbors algorithm
• Local binary patterns
• magnetic resonance imaging
• probabilistic neural network
• random forest
• Shap

• paper
• other

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