The following is a summary of “Limited added value of systematic spinal cord MRI vs brain MRI alone to classify patients with MS as active or inactive during follow-up,” published in the April 2025 ...

Your diaphragm contracts rhythmically and involuntarily (such as during sleep) due to signals from your brain. You can also voluntarily contract ... tomography (CT), magnetic resonance imaging (MRI), ...

This project uses Convolutional Neural Networks (CNN) to detect brain tumors from MRI images. The goal is to assist in fast and accurate diagnosis using deep learning.

Results: In order to validate the model, two public data sets including glioma, meningioma, and pituitary tumor were validated ... long-range dependence is proposed to classify brain tumor images from ...

Even when standard MRI exams appeared normal, the researchers found clear abnormalities using more ... repeated trauma seems to weaken the brain’s internal communication.” ...

This paper presents a novel two-stage approach for brain tumour segmentation in T1-weighted contrast-enhanced MRI (CE-MRI) scans, leveraging both YOLO(you only look once) and Modified U-Net. In the ...

Abstract: Brain MRI segmentation is critical for diagnosis and treatment planning, but existing methods are often limited by their task-specific designs and lack of generalizability. A significant ...

A deep learning solution for brain tumor segmentation using multi-modal MRI scans, integrating U-Net models, differential privacy, adversarial training, and explainability (Grad-CAM, attention scores) ...

Furthermore, in mild bTBI, standard anatomical imaging techniques (MRI and computed tomography) generally fail to show focal lesions and most of the symptoms present as subjective clinical functional ...