Classification of Brain Tumor using Hybrid Deep Learning Approach



Diagnosis of tumor at its early stage is the most challenging task for its treatment in the area of neurology. As, brain tumor is the most common problem in the world, so tremendous research is being carried out to find out the cancer during its onset stages. The task of diagnosis as well as its automation has been extremely difficult using conventional image processing methods. In view of this, a novel technique has been proposed based on convolutional neural network architecture to classify the brain tumor which assists radiologists and physicians to make their decision fast and accurate. The proposed deep learning structure helps to analyze and produce better feature maps to classify the variations in the normal and malignant cases. The proposed method i.e. Hybrid Deep Neural Network (H-DNN) architecture is the combination of two different DNN. First Deep Neural Network (DNN-1) uses the spatial texture information of the cranial Magnetic Resonance (MR) images, whereas in the second method Deep Neural Network (DNN-2) uses the frequency domain information of the MRI scans. Finally, we combine both neural networks to produce better classification result based on prediction score. The training input to the DNN-1 is the texture which is computed by Local Binary Patterns, whereas the DNN-2 uses the frequencies, which have being calculated by Wavelet Transformation as its training input. Here two Dataset have been used for the evaluation of the proposed model i.e. Real MRI dataset and BraTS 2012 MRI Dataset for T2- weighted MRI scans. In this study, the proposed model provides 98.7% classification accuracy, which outperforms the other methods as reported in the related work. Also comparisons of Accuracy, Sensitivity and Specificity of the proposed method has been done with DNN-1 and DNN-2 architecture to indicate that the reported model gives better results when compared to the other methods.


Brain tumor, convolutional neural network, deep learning, image classification, magnetic resonance imaging

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