Research on Brain Tumor Classification Method Based on Improved ResNet34 Network

TL;DR

This paper introduces an improved ResNet34-based model with multi-scale feature extraction and channel attention for brain tumor classification, achieving higher accuracy and fewer parameters than the original network.

Contribution

The paper presents a novel brain tumor classification model that enhances ResNet34 with multi-scale input, Inception v2 modules, and channel attention, improving accuracy and efficiency.

Findings

Achieved 98.8% classification accuracy.

Reduced model parameters to 80% of original.

Improved accuracy over standard ResNet34.

Abstract

Previously, image interpretation in radiology relied heavily on manual methods. However, manual classification of brain tumor medical images is time-consuming and labor-intensive. Even with shallow convolutional neural network models, the accuracy is not ideal. To improve the efficiency and accuracy of brain tumor image classification, this paper proposes a brain tumor classification model based on an improved ResNet34 network. This model uses the ResNet34 residual network as the backbone network and incorporates multi-scale feature extraction. It uses a multi-scale input module as the first layer of the ResNet34 network and an Inception v2 module as the residual downsampling layer. Furthermore, a channel attention mechanism module assigns different weights to different channels of the image from a channel domain perspective, obtaining more important feature information. The results after a five-fold crossover experiment show that the average classification accuracy of the improved network model is approximately 98.8%, which is not only 1% higher than ResNet34, but also only 80% of the number of parameters of the original model. Therefore, the improved network model not only improves accuracy but also reduces clutter, achieving a classification effect with fewer parameters and higher accuracy.