A Novel Brain Tumor Detection and Classification Technique Using an effective Deep Parallel CNN

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 main problem of CNN is that it is unable to extract unknown 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, and additionally, due to a large number of parameters, which can lead to overfitting. To address this, a novel lightweight Deep Parallel CNN is designed to extract the known features both locally and globally and introduce 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 (60×60) 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.