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Performance Analysis of VGG19 Deep Learning Network Based Brain Image Fusion

Performance Analysis of VGG19 Deep Learning Network Based Brain Image Fusion

Vijayarajan Rajangam, Sangeetha N., Karthik R., Kethepalli Mallikarjuna
Copyright: © 2023 |Pages: 23
ISBN13: 9781668475447|ISBN10: 1668475448|EISBN13: 9781668475454
DOI: 10.4018/978-1-6684-7544-7.ch070
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MLA

Rajangam, Vijayarajan, et al. "Performance Analysis of VGG19 Deep Learning Network Based Brain Image Fusion." Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention, edited by Information Resources Management Association, IGI Global, 2023, pp. 1375-1397. https://doi.org/10.4018/978-1-6684-7544-7.ch070

APA

Rajangam, V., N., S., R., K., & Mallikarjuna, K. (2023). Performance Analysis of VGG19 Deep Learning Network Based Brain Image Fusion. In I. Management Association (Ed.), Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention (pp. 1375-1397). IGI Global. https://doi.org/10.4018/978-1-6684-7544-7.ch070

Chicago

Rajangam, Vijayarajan, et al. "Performance Analysis of VGG19 Deep Learning Network Based Brain Image Fusion." In Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention, edited by Information Resources Management Association, 1375-1397. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-6684-7544-7.ch070

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Abstract

Multimodal imaging systems assist medical practitioners in cost-effective diagnostic methods in clinical pathologies. Multimodal imaging of the same organ or the region of interest reveals complementing anatomical and functional details. Multimodal image fusion algorithms integrate complementary image details into a composite image that reduces clinician's time for effective diagnosis. Deep learning networks have their role in feature extraction for the fusion of multimodal images. This chapter analyzes the performance of a pre-trained VGG19 deep learning network that extracts features from the base and detail layers of the source images for constructing a weight map to fuse the source image details. Maximum and averaging fusion rules are adopted for base layer fusion. The performance of the fusion algorithm for multimodal medical image fusion is analyzed by peak signal to noise ratio, structural similarity index, fusion factor, and figure of merit. Performance analysis of the fusion algorithms is also carried out for the source images with the presence of impulse and Gaussian noise.

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