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.
