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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. [ABSTRACT FROM AUTHOR]

Background: This research investigates the impact of pruning on reducing the computational complexity of a five-layered Convolutional Neural Network (CNN) designed for classifying MRI brain tumors. The study focuses on enhancing the efficiency of the model by removing less important weights and neurons through pruning., Objective: This research aims to analyze the impact of pruning on the computational complexity of a CNN for MRI brain tumor classification, identifying optimal pruning percentages to balance reduced complexity with acceptable classification performance., Methods: The proposed CNN model is implemented for the classification of MRI brain tumors. To reduce time complexity, weights and neurons of the trained model are pruned systematically, ranging from 0 to 99 percent. The corresponding accuracies for each pruning percentage are recorded to assess the trade-off between model complexity and classification performance., Results: The analysis reveals that the model's weights can be pruned up to 70 percent while maintaining acceptable accuracy. Similarly, neurons in the model can be pruned up to 10 percent without significantly compromising accuracy., Conclusion: This research highlights the successful application of pruning techniques to reduce the computational complexity of a CNN model for MRI brain tumor classification. The findings suggest that judicious pruning of weights and neurons can lead to a significant improvement in inference time without compromising accuracy.