MRI brain tumor detection and classification using parallel deep convolutional neural networks

Abstract

Convolutional neural network (CNN) is widely used to classify brain tumors with high accuracy. Since CNN collects features randomly without knowing the local and global features and causes overfitting problems, this research proposes a novel parallel deep convolutional neural network (PDCNN) topology to extract both global and local features from the two parallel stages and deal with the over-fitting problem by utilizing dropout regularizer alongside batch normalization. To begin, input images are resized and grayscale transformation is conducted, which helps to reduce complexity. After that, data augmentation has been used to maximize the number of datasets. The benefits of parallel pathways are provided by combining two simultaneous deep convolutional neural networks with two different window sizes, allowing this model to learn local and global information. Three forms of MRI datasets are used to determine the effectiveness of the proposed method. The binary tumor identification dataset-I, Figshare dataset-II, and Multiclass Kaggle dataset-III provide accuracy of 97.33%, 97.60%, and 98.12%, respectively. The proposed structure is not only accurate but also efficient, as the proposed method extracts both low-level and high-level features, improving results compared to state-of-the-art techniques.