Deep learning models achieve 97% accuracy in automated brain tumor detection

Researchers have developed a deep learning approach using Convolutional Neural Networks (CNNs) and Residual Networks (ResNets) to automate the detection of brain tumors in MRI images. The study applied transfer learning with pre-trained ResNet18 and ResNet50 models to classify scans, achieving high accuracy. Experiments on a dataset of nearly 4,000 images indicated that ResNet18 performed slightly better, reaching 97% accuracy, suggesting its effectiveness for medical data with limited samples. This method aims to provide a faster, more accurate, and cost-effective tool for early brain tumor diagnosis. AI

IMPACT Enhances diagnostic capabilities in medical imaging, potentially leading to earlier and more accurate detection of brain tumors.

RANK_REASON Academic paper detailing a new methodology for medical image analysis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• brain tumor
• convolutional neural network
• deep learning
• magnetic resonance imaging
• residual neural network
• ResNet18
• ResNet50
• transfer learning

• paper
• model release

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