Improving Efficiency of Brain Tumor Classification Models Using Pruning Techniques

Abstract

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.