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A Hybrid approach for PNN-Based MRI Brain Tumor Classification and Patient Detail Authentication Using Separable Reversible Hiding

Ms.K.Kothavari, R.Keerthana, M.Mariselvam,S.Kaveya, L.Mekala

The objective of this study is to employ Probabilistic Neural Network with MRI image and data processing techniques to implement an automated brain tumor classification and to propose a novel scheme for separable reversible data hiding in encrypted MRI images. Medical Resonance images (MRI) contain a noise caused by operator performance which can lead to serious inaccuracies in classification. The use of artificial intelligent techniques for instant, neural networks, and fuzzy logic show great potential in this field. Hence, in the classification process, Probabilistic Neural Network is applied. Decision making is performed in two stages: feature extraction using the principal component analysis and the Probabilistic Neural Network (PNN).In authentication phase, the medical practitioners can encrypt the original uncompressed image using an encryption key. Then, data-hiding is performed by compressing the least significant bits of the encrypted image to create a sparse space to accommodate patient information. If the receiver has the encryption key, the additional data and the original content can be encrypted and recovered without any error by exploiting the spatial correlation in natural image. Probabilistic Neural Network gives fast and accurate classification and isa promising tool for classification of the tumors and separable reverse hiding technique is an entrusted technique for information authentication.