An Effective Model to Detect and Classify Brain Tumor using Deep Convolutional Neural Network

Abstract

Since brain tumors have a variety of forms, it is crucial to determine the types of tumors. The convolutional neural network (CNN) can extract features automatically with high-performance accuracy. The inability of this method is that it is unable to extract unknowing features both locally and globally, with a large number of parameters. As a result, it must deal with computational complexity and take a long time to train, additionally, due to a large number of parameters that occur overfitting. To address this, this research proposes an effective model to detect and classify brain tumors using a deep convolutional neural network. A novel Deep Parallel CNN (DPCNN) is used to extract the features locally and globally effectively. The proposed method also uses global average pooling technology instead of the fully connected layer which effectively reduces the number of parameters and solves the overfitting problem. In the initial stage, resized data is augmented to enhance the number of images in the pre-processing step. Next, the augmented data is applied to the DPCNN to extract the features both locally and globally in the feature extraction step. Lastly, different forms of classifiers like Softmax, KNN, and SVM are used to classify the various forms of tumors from MRI scans in the classification step. The proposed DPCNN-SVM architecture achieved a classification accuracy of 97.7% which is compared with other existing models.