Medical image fusion is the process of creating a single image from the information included in several medical images of the same body region taken using various imaging modalities like Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Single-Photon Emission Computed Tomography (SPECT) And Positron Emission Tomography (PET). Many deep learning-based techniques for combining medical images have been presented, but creating suitable fusion rules is still challenging. Another issue with single-scale networks is inadequate feature extraction. Therefore, this paper proposes an efficient deep learning-based multi-level feature extraction network for Multi-modal Medical Image Fusion (MMIF). In this research, we propose a new advanced MMIF approach for medical image fusion. The proposed research employs two distinct enhanced Deep Learning (DL) algorithms for low and high-level feature extraction to fully extract and fuse significant and distinctive features from source images. The Improved GoogLeNet (IGoogLeNet) model is used to extract the low-level features, while Modified DenseNet-201 (MDenseNet-201) is used to extract the high-level features. Second, without creating new fusion rules, the proposed unique feature fusion module merely permits the fusion and enhancement of unique features. The Soft Attention (SA) fusion mechanism based on Softmax is used for fusing low-level and high features. Finally, the Modified Resblock module is developed for image reconstruction. For all image pairs, the proposed approach yields average values of 0.7671, 32.84, 19.316, 10.063, 0.8232, 5.3384, and 8.9874 for Edge-based Similarity Measure (QAB/F), Spatial Frequency (SF), Peak Signal-to-Noise Ratio (PSNR), Average Gradient (AG), Structural Similarity Index Measure (SSIM), Mutual Information (MI), and Gradient-based Metric (QG). Compared with the most recently published methods, the experimental findings show that our proposed fusion approach efficiently improves image contrast, brightness, and quality and better retains important information.

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