Abstract
Brain imaging technologies plays an important role in medical diagnosis by providing new views of the brain anatomy giving greater insight into brain condition and functions. Image processing is used in the area of medical science to assist the early detection and treatment of life-critical illness. In this paper, cancer detection based on the brain magnetic resonance imaging (MRI) images using a combination of convolutional neural network (CNN) and sparse stacked auto encoder is presented. This combination is found to provide a significant effect in improving the accuracy and effectiveness of the classification process. The proposed method is coded in MATLAB and verified with the dataset consisting of 120 MRI images. The results obtained had shown that the proposed classifier is very much effective in classifying and grading the brain tumor MRI images.
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