Intelligent brain hemorrhage diagnosis using artificial neural networks

Abstract

Brain hemorrhage is a type of stroke which is caused by an artery in the brain bursting and causing bleeding in the surrounded tissues. Diagnosing brain hemorrhage, which is mainly through the examination of a CT scan enables 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. Though a lot of research on medical image processing has been done, still there is opportunity for further research in the area of brain hemorrhage diagnosis 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. Additionally many of the existing approaches address the diagnosis of a limited no of brain hemorrhage types. This project investigates the possibility of diagnosing brain hemorrhage using an image segmentation of CT scan images using watershed method and 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 a learning tool for trainee radiologists to minimize errors in current methods. The prototype developed using Matlab can help medical students to practice the related concepts they learn using an image guide with examples for surgeries and surgical simulation. System was evaluated by the domain experts, like radiologists, intended users such as medical students as well as by technical experts. The prototype developed was successful since it was being evaluated as credible, innovative and useful software for the students in the field of radiology while 100% of the evaluators mentioned the diagnosis accuracy is acceptable.