Ramana, K. V.; Reddy, L . Pratap
April 2009
Allied Academies International Conference: Proceedings of the Ac;Apr2009, Vol. 13 Issue 1, p52
Conference Proceeding
Despite advances in medical sciences, many patients cannot gain benefits from developed methods, especially health care specialists in areas such as, in our case, brain diagnosis. Lack of proper system detection is increasing the death rates in the patients. Brain hemorrhage is one of the most frequent forms of hemorrhages among the people all over the world. Previous systems has tackled the problem of detection of brain hemorrhage through Magnetic Resonance Imaging (MRI) images. The social problem this research addressed is the lack of system for detection and classification of brain haemorrhages. This research explores the application of artificial neural networks (ANN) in medical diagnosis and classification computerized algorithms for diagnosing brain hemorrhage by examining Computer Tomography (CT) images of patients. After employing a neural network based Methodology; the results indicate that the system uses a classification approach and has an accuracy of 73% detection when evaluated against a specialist recommended medical referral decision. The input dataset to the trained Artificial Neural Network obtained from CT scanned images. The results of this study could potentially useful to develop further detection systems for medical community in terms of predicting patients who are likely to have brain hemorrhages.


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