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Machine Learning Approaches Towards Medical Images
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Author(s): Gayathri S. P. (The Gandhigram Rural Institute (Deemed), India), Siva Shankar Ramasamy (International College of Digital Innovation (ICDI), Chiang Mai University, Thailand)and Vijayalakshmi S. (Department of Data Science, Christ University (Deemed), India)
Copyright: 2023
Pages: 22
Source title:
Structural and Functional Aspects of Biocomputing Systems for Data Processing
Source Author(s)/Editor(s): U. Vignesh (Vellore Institute of Technology, Chennai, India), R. Parvathi (Vellore Institute of Technology, India)and Ricardo Goncalves (Department of Electrical and Computer Engineering (DEEC), NOVA School of Science and Technology, NOVA University Lisbon, Portugal)
DOI: 10.4018/978-1-6684-6523-3.ch006
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Abstract
Clinical imaging relies heavily on the current medical services' framework to perform painless demonstrative therapy. It entails creating usable and instructive models of the human body's internal organs and structural systems for use in clinical evaluation. Its various varieties include signal-based techniques such as conventional X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US) imaging, and mammography. Despite these clinical imaging techniques, clinical images are increasingly employed to identify various problems, particularly those that are upsetting the skin. Imaging and processing are the two distinct patterns of clinical imaging. To diagnose diseases, automatic segmentation using deep learning techniques in the field of clinical imaging is becoming vital for identifying evidence and measuring examples in clinical images. The fundamentals of deep learning techniques are discussed in this chapter along with an overview of successful implementations.
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