Introduction Multimodal medical image fusion technology generates new images containing more accurate disease information by fusing different modal images. It not only improves the accuracy and efficiency of diagnosis but also provides strong support for the formulation of treatment plans. Meanwhile

it also shows great potential value in disease monitoring

personalized medicine

and clinical research. Although different multimodal medical image fusion methods have been presented

most of them are hindered by information loss

blurred edges

and low fusion efficiency. Methods To solve these problems

this paper proposes a multiscale residual dense fusion network (MRDFN) for multimodal medical image fusion. MRDFN integrates the strengths of both the multiscale residual network and dense network to achieve feature extraction and fusion. Results Experiments show that the fusion images of the proposed method are superior to the reference methods in terms of edge intensity

detail definition

and objective metrics. The comparative analysis of these fusion metrics proves that the fusion image quality of MRDFN is better than that of the reference methods. The suggested method achieves higher values in average gradient

standard deviation

spatial frequency

and visual information fidelity for fusion

with average gradient reaching 2.0 times the average of the comparison algorithms

standard deviation 1.2 times

spatial frequency 2.3 times

and visual information fidelity for fusion 1.3 times. Conclusions The findings in this study demonstrate that MRDFN outperforms other approaches discussed in the analysis

particularly in objective metrics and detailed information

and the average fusion time of MRDFN is lower than that of most reference methods

demonstrating effective multimodal medical image fusion.