Machine learning and deep learning techniques for breast cancer detection using ultrasound imaging

Title

Machine learning and deep learning techniques for breast cancer detection using ultrasound imaging

Creator

Ajmani P.; Sharma V.; Rai R.H.; Kalra S.

Description

One of the greatest causes leading to death in women is breast cancer. Its prompt and precise identification can reduce the mortality risk associated with the disease. With the help of computer-based detection, radiologists can identify irregularities. To identify and diagnose numerous illnesses and anomalies, medical photographs are sources of important information. Various techniques help radiographers to examine the internal system, and these techniques have generated a significant amount of attention across several fields of research. Each of these approaches holds a great deal of relevance in many healthcare sectors. Using artificial intelligence techniques, this article aims to present a study that highlights current developments in the detection and classification of breast cancer. The categorization of breast cancer using many medical imaging modalities is discussed in this article. It initially offers a summary of the various machine learning methodologies, followed by a summary of the various deep learning algorithms used in the detection and characterization of metastatic breast tumors. To give an insight into the field, we also give a quick summary of the various imaging techniques. The chapter concludes by summarizing the upcoming developments and difficulties in the diagnosis and classification of breast cancer. 2024 Elsevier Inc. All rights reserved.

Source

Computational Intelligence and Modelling Techniques for Disease Detection in Mammogram Images, pp. 235-257.

Date

2023-01-01

Publisher

Elsevier

Subject

Artificial intelligence; Breast cancer classification; Computer-aided diagnosis (CAD); Deep learning; Magnetic resonance imaging (MRI); Mammogram; Radiology; Ultrasound

Coverage

Ajmani P., Vivekananda Institute of Professional Studies-TC, GGSIPU, New Delhi, India; Sharma V., Computer Science Department, CHRIST (Deemed to be University), Delhi-NCR, India; Rai R.H., School of Physiotherapy, Delhi Pharmaceutical Sciences and Research University (DPSRU), New Delhi, India; Kalra S., School of Physiotherapy, Delhi Pharmaceutical Sciences and Research University (DPSRU), New Delhi, India

Rights

Restricted Access

Relation

ISBN: 978-044313999-4; 978-044314000-6

Format

Online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.1016/B978-0-443-13999-4.00002-X

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191112356&doi=10.1016%2fB978-0-443-13999-4.00002-X&partnerID=40&md5=22b6f07587ea0ea06b64103dc34d1f79