Multi View Image Fusion Using Ensemble Deep Learning Algorithm for Mri and CT Images

Abstract

Medical image fusions are crucial elements in image based health care diagnostics or therapies, and generically applications of computer visions. However, majority of existing methods suffer from noise distortion that affect the overall output. When pictures are distorted by noises, classical fusion techniques perform badly. Hence, fusion techniques that properly maintain information comprehensively from multiple faulty pictures need to be created. This work presents ESLOs (Enhanced Lion Swarm Optimizations) with EDL (Ensemble Deep Learning) to address the aforementioned issues. The primary steps in this study include image fusions, segmentation, noise reduction, feature extraction, picture classification, and feature selection.AMFs (Adaptive Median Filters) are first used for noise removal in sequence to enhance image quality by eliminating noises. The MRIs and CTS images are then segmented using the RKMC algorithm to separate the images into their component regions or objects. Images in black and white are divided into image. In the white image, the RKMC algorithm successfully considered the earlier tumour probability. The next step is feature extraction, which is accomplished by using the MPCA (Modified Principal Component Analysis) to draw out the most informative aspects of the images. Then, ELSOs algorithm is applied for optimal feature selection which is computed by best fitness values. After that, multi view image fusions of multi modal images derive lower, middle and higher level images contents. It is done by using DCNNs (Deep Convolution Neural Networks) and TAcGANs (Tissue-Aware conditional Generative Adversarial Networks) algorithm which fuses the multi view features and relevant image features and it is used for real time applications. The results of this study implies that proposed ELSO+EDL algorithm results in better performances in terms of higher values of accuracies, PSNR and lower RMSE, MAPE with faster executions when compared to other existing algorithms.