Machine learning approaches towards medical images

Title

Machine learning approaches towards medical images

Creator

Gayathri S.P.; Ramasamy S.S.; Vijayalakshmi S.

Description

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. 2023, IGI Global. All rights reserved.

Source

Structural and Functional Aspects of Biocomputing Systems for Data Processing, pp. 124-145.

Date

2023-01-01

Publisher

IGI Global

Coverage

Gayathri S.P., The Gandhigram Rural Institute (Deemed), India; Ramasamy S.S., International College of Digital Innovation (ICDI), Chiang Mai University, Thailand; Vijayalakshmi S., Department of Data Science, Christ University (Deemed), India

Rights

Restricted Access

Relation

ISBN: 978-166846525-7; 166846523X; 978-166846523-3

Format

Online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.4018/978-1-6684-6523-3.ch006

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151459822&doi=10.4018%2f978-1-6684-6523-3.ch006&partnerID=40&md5=1ea6013d0e0d7e46f89f150c174c3b68