Title:Improving Efficiency of Brain Tumor Classification Models Using Pruning Techniques
Volume: 20
Author(s): M. Sivakumar*S.T. Padmapriya
Affiliation:
- Department of Computer Science and Engineering, Thiagarajar College of Engineering, Madurai, Tamil Nadu, India
Keywords:
Convolutional neural networks (CNN), Classification, Pruning, Brain tumor, Neural network, MRI.
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.
