Two stage multi-modal medical image fusion with marine predator algorithm-based cascaded optimal DTCWT and NSST with deep learning

•The highlights of the article are given below for your kind perusal. Kindly, consider and forward my article for further processes.•To design an efficient multimodal medical image fusion model in two stages using diverse wavelet transform and deep learning architecture with the optimization algorithm for enhancing the image quality of medical images to take better decisions in the earlier treatment of the patients.•To develop an enhanced wavelet transform technique named Optimized Dual-Tree Complex Wavelet Transform (DTCWT) for decomposing the images into the high and low-frequency coefficient to improve the quality without reducing the information in it based on the support of developed Enhanced Marine Predator algorithm (EMPO).•To integrate the Optimized Deep Neural Network (ODNN) for fusing the high and low-frequency coefficient images in the second stage for elevating the fusion performance of the developed TSMMIF model with the aid of suggested EMPO.•To implement an enhanced optimization algorithm named EMPO for tuning the number of levels in DTCWT for improving the performance at the first stage of image fusion and also optimizing the hidden neurons in the DNN for elevating the performance of the second stage image fusion.•To test the developed TSMMIF model by comparing the conventional algorithms to reveal the efficiency of the developed model. Multimodal medical image fusion is highly essential to minimize the redundancy rate at the time of getting the required information through the input images by diverse medical imaging sensors. The main intention is to get an individual fused image that is highly informative for supporting the clinical evaluation. In this research work, Two Stage Multi-modal Medical Image Fusion is presented via the Cascaded Optimal Dual-Tree Complex Wavelet Transform (O-DTCWT) and the Non-Sub-sampled Shearlet Transform with two different medical image modalities. In the first stage, the collected image 1 and image 2 are separately given to the Optimal DTCWT for signal decomposition, which divides the high and low-frequency components. The high-frequency components are fused via fuzzy logic, whereas the Maximum rule is used to fuse the low frequency components. Then, the fused images are given to the inverse DTCWT for image reconstruction. In image fusion process, the DTCWT method is suitable for shift variance and multi-dimensionality. After, the reconstructed image is fed to the second stage of image decomposition. The NSST