Brain Tumor Classification Using Relief Algorithm Based Convolutional Neural Network

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Dr. R. Karthik, et. al.

Abstract

The brain is another vital organ in humans for running a standard life. In this fast-paced world, brain damage is caused by eating habits and their personal lifestyle. In such a case, the current development in medical and imaging technology could be a boon for treating diseases. At the early stage, the tumour part is not very visible, which can be identified by the variation in texture and the small limits of the image. The conventional method used the edge representation and deep learning convolutional neural network for the classification process. Its accuracy of classification is solely depends on the parameters of the network. This problem is overcome in the first phase of research by using gray scale properties based feature extraction and Galactic swarm based Convolutional neural network for the tumour classification. But, it requires high computational time for attribute selection and it does not describe about the tumour pattern. Hence, in this, a fast processing and pattern based convolutional neural network is proposed. Here, the images are subjected to pre-processing using inverse filtering and then the pattern oriented features were extracted using local binary pattern and histogram oriented gradients. Then, the extracted features are ranked using the Relieff algorithm. Then, top most ranked feature is used for the classification process using convolutional neural network. The whole process is realized on the figshare tumour dataset in MATLAB R2018a under windows10 environment. Then, its performance is compared with the existing techniques based on accuracy, sensitivity and specificity. The proposed Pattern based CNN can achieve high accuracy of 99.7% with smaller processing time of less than 10 minutes as compared to existing techniques.


 

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