Medical image registration and classification using smell agent rat swarm optimization based deep Maxout network

Abstract

Medical image registration (MIR) is a crucial task in clinical image processing, involving the alignment of images from different modalities, such as magnetic resonance imaging (MRI) and computed tomography (CT), across various time points and subjects. Despite numerous advancements, no universal method caters to all MIR applications. This paper introduces the smell agent rat swarm optimization based deep Maxout network (SARSO-DMN) for MIR and classification. This work aims to enhance the accuracy and efficiency of medical image alignment and classification, addressing the challenges posed by diverse imaging modalities and temporal variations. The problem involves effectively registering CT and MRI images, followed by inhale and exhale classification. The proposed approach begins with feeding the input images into a convolutional neural network (CNN), followed by applying a deformation field to generate an intermediate output (output-1). This output, along with the input MRI images, is further processed by a CNN to produce output-2. Subsequently, output-2 and the input MRI image are subjected to another CNN, resulting in the final registered image. The classification phase utilizes a DMN optimized by the SARSO algorithm, which combines smell agent optimization (SAO) and rat swarm optimizer (RSO). The results demonstrate that SARSO-DMN achieves a maximum accuracy of 90.7%, a minimum false positive rate (FPR) of 11.3%, and a maximum true positive rate (TPR) of 91.2%. The SARSO-DMN approach provides a robust solution for MIR and classification, leveraging advanced optimization techniques to enhance performance.