Exploration of Multi-Scale Image Fusion Systems in Intelligent Medical Image Analysis
This work addresses the need for accurate and efficient brain cancer diagnosis through medical imaging, though it appears incremental as it builds on existing U-Net methods.
The paper tackled brain tumor segmentation in MRI images by combining a U-Net-based algorithm with residual networks, context enhancement modules, and void space convolution pooling pyramids, achieving an accuracy of 0.9851 and improving 3D reconstruction precision.
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.