Abstract:
Diagnosing brain hemorrhage, which is a condition caused by a brain artery busting and causing bleeding is currently done by medical experts using a CT scan. Periodic examination of scans enable the accurate prediction of disease and the extraction of reliable and robust measurement for patients in order to describe the morphological changes in the brain as the recovery progresses. Prior attempts to use medical image processing techniques to extract relevant information from a CT scan has shortcoming due to the low accuracy level in the current methods and algorithms, coding complexity of the developed approaches, impracticability in the real environment, and lack of other enhancements which may make the system more interactive and useful. This research investigates the possibility of diagnosing brain hemorrhage using an image segmentation of CT scan images using watershed method, feeding of the appropriate inputs extracted from the brain CT image to an artificial neural network for classification. The output generated as the type of brain hemorrhages, can be used to verify expert diagnosis and also as learning tool for trainee radiologists to narrow down the errors in current methods.
