Medical imaging provides proper diagnosis of brain tumour. Various techniques are implemented to detect the brain tumour from MRI images. One among them is the Denoising wavelet transform (DWT) method which is used to improve the accuracy of a prediction model by making use of MRI images in order to predict the overall survival time of brain tumour patients. Wavelet transform method detects the location and size of the tumour. The proposed methodology consists of image acquisition, Calculation of tissue density maps, statistical analysis. MRI provides generous information about the human soft tissue, which helps in the recognition of brain tumour. Image Segmentation categorises pixels into sections and hence defines the object regions. This paper proposes the image and feature fusion techniques to improve the accuracy of the prediction model.

Machine learning, Denoising wavelet transform, MRI images, Histogram, Glioma brain tumour, Linear Regression

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