Convolutional Neural Networks (CNN) with Quantum-Behaved Particle Swarm Optimization (QPSO)-Based Medical Image Fusion

Abstract

Medical imaging fusion is the process of combining pictures from various imaging modalities to create a single image that may be used in clinical settings. Robust methods for merging image data from several modalities are being developed in the field of multimodal medical imaging. Deep learning (DL) has been widely researched in two areas: pattern recognition and image processing. We will demonstrate a multimodal image fusion with DL implementation that considers the characteristics of medical diagnostic imaging as well as the demands of clinical practice. For the past three years, pixel-level picture fusion has been a hot topic. This paper proposes a new multimodal medical picture fusion technique for a wide range of medical diagnostic challenges. Image fusion is crucial in biomedical research and clinical diagnostics for biomedical image processing and therapy planning. The most convincing argument for fusion is obtaining a significant amount of critical information from the input photographs. We show how a well-organized multimodal medical image fusion technique can be utilized to integrate computed tomography (CT) and magnetic resonance imaging (MRI) data in this study. Using convolutional neural networks (CNNs), the quantum-behaved particle swarm optimization (QPSO) algorithm was used to create a method for integrating multimodal medical pictures. In order to improve the overall quality and efficiency of QPSO, it was chosen to add the metrics of image entropy, standard deviation, average gradient (AG), spatial frequency (SF), and visual information fidelity (VIF). In experiments, multimodal medical images are utilized to evaluate a variety of parameters, including performance and algorithm stability. When compared to the other possibilities, the recommended technique outperformed them in the evaluations. On a range of quantitative metrics, this method outperforms the alternatives.