A NOVEL APPROACH TO AUTOMATED BRAIN TUMOR CLASSIFICATION USING PROBABILISTIC NEURAL NETWORK

Conventional methods of monitoring and diagnosing the diseases rely on detecting the presence of particular features by a human observer. Due to large number of patients in intensive care units and the need for continuous observation of such conditions, several techniques for automated diagnostic systems have been developed in recent years to attempt to solve this problem. Such techniques work by transforming the mostly qualitative diagnostic criteria into a more objective quantitative feature classification problem.Probabilistic Neural Network (PNN) with image and data processing techniques will be employed to implement an automated brain tumor classification. The conventional method for Medical Resonance (MR) brain images classification and tumors detection is by human inspection. Operator-assisted classification methods are impractical for large amounts of data and are also non-reproducible. Medical Resonance (MR) images contain a noise caused by operator performance which can lead to inaccuracies in classification. The use of artificial intelligent techniques like neural networks, and fuzzy logic has shown great potential in this field.