Convolutional neural network classification of ultrasound parametric images based on echo-envelope statistics for the quantitative diagnosis of liver steatosis.

Journal: Journal of medical ultrasonics (2001)

PMID: 39579195

Abstract

PURPOSE: Early detection and quantitative evaluation of liver steatosis are crucial. Therefore, this study investigated a method for classifying ultrasound images to fatty liver grades based on echo-envelope statistics (ES) and convolutional neural network (CNN) analyses.

Authors

Akiho Isshiki

Department of Medical Engineering, Graduate School of Science and Engineering, Chiba University, Chiba, 263-8522, Japan.
Kisako Fujiwara

Department of Gastroenterology, Graduate School of Medicine, Chiba University, Chiba, 260-8677, Japan.
Takayuki Kondo

Department of Gastroenterology, Graduate School of Medicine, Chiba University, Chiba, 260-8677, Japan.
Kenji Yoshida

Department of Oral and Maxillofacial Surgery, School of Dentistry, Aichi Gakuin University, Nagoya, Japan.
Tadashi Yamaguchi

Ultrasound Center, Chiba University Hospital, Chiba, 260-8677, Japan.
Shinnosuke Hirata

Ultrasound Center, Chiba University Hospital, Chiba, 260-8677, Japan. shin@chiba-u.jp.

Keywords

Adult Convolutional Neural Networks Fatty Liver Female Humans Image Interpretation, Computer-Assisted Liver Magnetic Resonance Imaging Male Middle Aged Neural Networks, Computer Ultrasonography

External Resources

View on PubMed Access via DOI PubMed (39579195)

Convolutional neural network classification of ultrasound parametric images based on echo-envelope statistics for the quantitative diagnosis of liver steatosis.

Abstract

Authors

Keywords

External Resources

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