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A PCCN-Based Centered Deep Learning Process for Segmentation of Spine and Heart: Image Deep Learning
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Author(s): K. Uday Kiran (Koneru Lakshmaiah Education Foundation, India), Gowtham Mamidisetti (Bhoj Reddy Engineering College for Women, India), Chandra shaker Pittala (MLR Institute of Technology, India), V. Vijay (Institute of Aeronautical Engineering, India)and Rajeev Ratna Vallabhuni (Independent Researcher, USA)
Copyright: 2022
Pages: 12
Source title:
Handbook of Research on Technologies and Systems for E-Collaboration During Global Crises
Source Author(s)/Editor(s): Jingyuan Zhao (University of Toronto, Canada)and V. Vinoth Kumar (Jain University, India)
DOI: 10.4018/978-1-7998-9640-1.ch002
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Abstract
The spinal cord and heart in the body are major organs. Diagnosis of diseases in these organs is very complex using MRI and CT images. The conventional methods like post segmentation, pre-image processing, and text feature extraction mechanisms cannot handle accurate diagnosis. Therefore, advanced techniques are needed. In this work, pixel-based convolution neural networks with centered deep learning processes are proposed to cross over the problems. The projected PCNN has four pixel-based convolution neural networks. Here disease objects are identified through grading framework. The entire mechanism is working based on sequential part of PCNN segmentation process. The spinal cord and heart image MRI-based diagnosis process is very difficult with conventional methods. But the proposed method provides accurate results and outperforms the standard methodology performance measures in accuracy, precision, and F1score.
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