Hemorrhage Detection from Whole-Body CT Images Using Deep Learning

In medical applications, deep learning has shown to be a powerful tool, especially when it comes to identifying patterns in healthcare datasets. Radiologists’ evaluation of CT images is crucial to the prompt identification of cerebral bleeding. The dataset used in this investigation included 3000 patients’ full-body DICOM CT scans. After segmenting these scans to separate the brain pictures, clustering was used to put them in groups according to visual similarity. This method increases the possibility of reliably and effectively identifying cerebral hemorrhage, which may have an effect on patient outcomes. Further convolutional neural network (CNN) is applied to find patterns in Brain CT scans of patients to correctly detect internal bleeding and classify hemorrhage and nonhemorrhage images.