Brain haemorrhage detection using CNN

Detecting brain hemorrhages using Convolutional Neural Networks (CNNs) has emerged as a powerful approach in medical imaging. CNNs, which excel in image recognition tasks, can analyze CT scans and MRI images to identify abnormal bleeding in the brain. By training on large datasets of annotated medical images, these neural networks learn to distinguish between healthy brain tissue and areas affected by hemorrhages. The architecture of CNNs, characterized by convolutional layers, pooling layers, and fully connected layers, enables them to extract intricate features and patterns from complex images. This automated detection not only enhances diagnostic accuracy but also significantly reduces the time required for radiologists to evaluate images, facilitating quicker treatment decisions and ultimately improving patient outcomes. As research progresses, the integration of CNNs into clinical workflows promises to revolutionize the way brain injuries are diagnosed and managed.