Related Applications of Deep Learning Algorithms in Medical Image Fusion Systems

As the continuous advancement of medical technology, image fusion technology has also been used in it. However, current medical image fusion systems still have drawbacks such as low image clarity, low accuracy, and slow computing speed. To address this drawback, this study utilized speeded up robust features image recognition algorithms to optimize deep residual network algorithms and proposed an optimization algorithm based on residual network deep learning algorithms. Based on this optimization algorithm, a medical image fusion system was constructed. Comparative experiments were organized on the improved algorithm, and the experiment outcomes denoted that the accuracy of image feature extraction was 0.98, the average time for feature extraction was 0.12 seconds, and the extraction capability was significantly better than that of the comparative algorithms HPF-CNN, PSO and PCA-CNN. Subsequently, experiments were conducted on the image fusion system, and the outcomes denoted that the accuracy and clarity of the fused images were 0.98 and 0.97, respectively, which were superior to other systems. The above outcomes indicate that the proposed medical image fusion system based on optimized deep learning algorithms can not only improve the speed of image fusion, but also enhance the clarity and accuracy of fused images. This study not only improves the accuracy of medical diagnosis, but also provides a theoretical basis for the field of image fusion.