Exploration of Multi-Scale Image Fusion Systems in Intelligent Medical Image Analysis

TL;DR

This paper develops an advanced MRI brain tumor segmentation algorithm combining U-Net, residual networks, and multi-scale processing, achieving high accuracy and improved 3D reconstruction for better diagnosis.

Contribution

It introduces a novel multi-scale segmentation method integrating residual networks and void space convolution pooling into U-Net for enhanced brain tumor MRI analysis.

Findings

Achieved 98.51% segmentation accuracy.

Enhanced 3D tumor reconstruction precision.

Improved image classification efficiency.

Abstract

The diagnosis of brain cancer relies heavily on medical imaging techniques, with MRI being the most commonly used. It is necessary to perform automatic segmentation of brain tumors on MRI images. This project intends to build an MRI algorithm based on U-Net. The residual network and the module used to enhance the context information are combined, and the void space convolution pooling pyramid is added to the network for processing. The brain glioma MRI image dataset provided by cancer imaging archives was experimentally verified. A multi-scale segmentation method based on a weighted least squares filter was used to complete the 3D reconstruction of brain tumors. Thus, the accuracy of three-dimensional reconstruction is further improved. Experiments show that the local texture features obtained by the proposed algorithm are similar to those obtained by laser scanning. The algorithm is improved by using the U-Net method and an accuracy of 0.9851 is obtained. This approach significantly enhances the precision of image segmentation and boosts the efficiency of image classification.