Artificial Intelligence-Based Multimodal Medical Image Fusion Using Hybrid S2 Optimal CNN

In medical applications, medical image fusion methods are capable of fusing the medical images from various morphologies to obtain a reliable medical diagnosis. A single modality image cannot provide sufficient information for an exact diagnosis. Hence, an efficient multimodal medical image fusion-based artificial intelligence model is proposed in this paper. Initially, the multimodal medical images are obtained for an effective fusion process by using a modified discrete wavelet transform (MDWT) thereby attaining an image with high visual clarity. Then, the fused images are classified as malignant or benign using the proposed convolutional neural network-based hybrid optimization dynamic algorithm (CNN-HOD). To enhance the weight function and classification accuracy of the CNN, a hybrid optimization dynamic algorithm (HOD) is proposed. The HOD is the integration of the sailfish optimizer algorithm and seagull optimization algorithm. Here, the seagull optimizer algorithm replaces the migration operation toobtain the optimal location. The experimental analysis is carried out and acquired with standard deviation (58%), average gradient (88%), and fusion factor (73%) compared with the other approaches. The experimental results demonstrate that the proposed approach performs better than other approaches and offers high-quality fused images for an accurate diagnosis.

Automated summary

In this paper, a multimodal medical image fusion-based artificial intelligence model is proposed, which utilizes a modified discrete wavelet transform to obtain multimodal medical images and a hybrid optimization dynamic algorithm for image fusion and malignant/benign classification. The experimental results demonstrate that the proposed approach outperforms other methods and provides high-quality fused images for accurate diagnosis.