Intercranial Hemorrhage Detection Using CNN

Brain hemorrhages is a critical medical disorder that needs to be diagnosed as soon as possible in order to allow for immediate medical attention.In the current study, we provide a machine approach to learning to detect various types of brain hemorrhage including subdural, epidural, and intracerebral hemorrhage using a Convolutional Neural Network (CNN) based ResNet-100 framework.We train and evaluate our CNN model using a diverse data set of brain scans of normal cases and cases of stroke types.The application of the ResNet-100 algorithm comes from its ability to handle deep learning tasks and capture complex features in medical images efficiently.Through extensive training and validation, our model achieves an impressive 95% accuracy in classifying brain scans as normal or indicative of subdural, epidural, or intracerebral hemorrhage The high accuracy shows how we machine learning approach is effective for multiclass brain hemorrhage detection.The proposed system has the potential to help radiologists and healthcare professionals to accurately and effectively identify various types of brain hemorrhage from medical images Using the capabilities of CNN and ResNet-100, our method enables early detection and timely intervention, improving patient outcomes.