Abstract:
Brain tumors were the most common kind of tumor in humans. Brain tumors can be detected from various imaging
technologies. The proposed research work strives to improve the prediction accuracy of brain tumor detection and segmentation from
MRI of human head scans by using a novel activation function E-Tanh. The role of activation functions is to perform computations
and make decisions in artificial neural networks (ANN). We developed three ANN models for brain tumor detection by modifying the
hidden layers. We have trained these ANN models using the E-Tanh activation function and evaluated their performance. This novel
activation function achieved 98% prediction accuracy for the MRI brain tumor image detection neural network model, which was
higher than the existing activation functions. We also have segmented brain tumors from the BraTS2020 dataset by using this activation
function in U-Net-based architecture. We attained dice scores of 83%, 95%, and 85% for the whole, core, and enhancing tumors, which
are significantly higher than the ReLU activation function.
