A Smart Device Employs Vgg-16 to Identify Brain Stroke Using CT Scan Images

Because brain stroke is a potentially fatal medical illness, prompt and correct diagnosis is essential for prompt health treatment, better patient outcomes, and improved mortality. It can take a long time and be susceptible to human error to manually evaluate medical imaging data conventionally for stroke diagnosis. This study examines how Convolutional Neural Networks (CNNs) can be used to dynamically classify brain strokes in order to overcome these issues. The suggested study employs the VGG-16 CNN model to classify CT scans of the brain following a stroke. For this challenge, the primary extractor is the VGG-16 model, which is initialized using pre-trained weights from the ImageNet dataset. This allows transfer learning to make use of the network's learned features. By using CT scan pictures to diagnose cerebral stroke, the suggested method demonstrates the potential of VGG-16 as a crucial tool for helping radiologists and other medical professionals. The incorporation of these technologies into clinical processes may help to speed up the diagnosis of strokes, allowing for quicker treatments and better patient care.