Medical image analysis based on deep learning approach

• Published in: Multimedia tools and applications Vol. 80; no. 16; pp. 24365 - 24398
• Main Authors: Puttagunta, Muralikrishna, Ravi, S.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2021; Springer Nature B.V
• Subjects: Artificial neural networks; Computed tomography; Computer Communication Networks; Computer Science; Computer vision; Data Structures and Information Theory; Deep learning; Digital imaging; Image analysis; Image classification; Image segmentation; Machine learning; Medical imaging; Medical research; Multimedia Information Systems; Neural networks; Special Purpose and Application-Based Systems; Deep learning; Medical images; Segmentation; Convolutional neural networks; Detection; Classification
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-021-10707-4

Medical imaging plays a significant role in different clinical applications such as medical procedures used for early detection, monitoring, diagnosis, and treatment evaluation of various medical conditions. Basicsof the principles and implementations of artificial neural networks and deep learning are essential for understanding medical image analysis in computer vision. Deep Learning Approach (DLA) in medical image analysis emerges as a fast-growing research field. DLA has been widely used in medical imaging to detect the presence or absence of the disease. This paper presents the development of artificial neural networks, comprehensive analysis of DLA, which delivers promising medical imaging applications. Most of the DLA implementations concentrate on the X-ray images, computerized tomography, mammography images, and digital histopathology images. It provides a systematic review of the articles for classification, detection, and segmentation of medical images based on DLA. This review guides the researchers to think of appropriate changes in medical image analysis based on DLA.