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AI enhances MRI brain tumor detection with deep learning and SHAP analysis

By PulseAugur Editorial · [1 sources] ·

Researchers have developed an enhanced method for detecting brain tumors using Magnetic Resonance Imaging (MRI) by combining deep learning techniques with explainable AI. The study explored various feature representation schemes, including Local Binary Patterns (LBP), Gabor filters, and Convolutional Neural Networks (CNNs), alongside several learning algorithms. Results showed that LBP combined with Support Vector Classifiers (SVC) and CNNs achieved high accuracy, with CNN reaching 98.9% on a large dataset. SHAP analysis was employed to identify the most impactful features for classification, confirming the significant contribution of LBP. AI

IMPACT This research could lead to more accurate and efficient diagnostic tools for brain tumor detection, improving patient outcomes.

RANK_REASON The cluster contains an academic paper detailing a novel research methodology for medical image analysis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI enhances MRI brain tumor detection with deep learning and SHAP analysis

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The cluster contains an academic paper detailing a novel research methodology for medical image analysis. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Asif Rahman, Maqsood Hayat, Nadeem Iqbal, Fawaz Khaled Alarfaj, Salem Alkhalaf, Fahad Alturise ·

    Enhanced MRI brain tumor detection using deep learning in conjunction with explainable AI SHAP based diverse and multi feature analysis

    arXiv:2611.07843v1 Announce Type: cross Abstract: Recent innovations in medical imaging have markedly improved brain tumor identification, surpassing conventional diagnostic approaches that suffer from low resolution, radiation exposure, and limited contrast. Magnetic Resonance I…

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