This paper is in the following e-collection/theme issue:

Machine Learning (1465) New Methods (944) Decision Support for Health Professionals (1185) Imaging Informatics (193) Registered Report (561)

Published on in Vol 10, No 11 (2022): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/40878, first published .
Automatic Screening of Pediatric Renal Ultrasound Abnormalities: Deep Learning and Transfer Learning Approach

Automatic Screening of Pediatric Renal Ultrasound Abnormalities: Deep Learning and Transfer Learning Approach

Automatic Screening of Pediatric Renal Ultrasound Abnormalities: Deep Learning and Transfer Learning Approach

Authors of this article:

Ming-Chin Tsai1 Author Orcid Image ;   Henry Horng-Shing Lu2 Author Orcid Image ;   Yueh-Chuan Chang3 Author Orcid Image ;   Yung-Chieh Huang1, 4 Author Orcid Image ;   Lin-Shien Fu1, 4, 5 Author Orcid Image
Article Authors Cited by (5) Tweetations (2) Metrics

Journals

  1. Bhandari M, Yogarajah P, Kavitha M, Condell J. Exploring the Capabilities of a Lightweight CNN Model in Accurately Identifying Renal Abnormalities: Cysts, Stones, and Tumors, Using LIME and SHAP. Applied Sciences 2023;13(5):3125 View
  2. Sloan M, Li H, Lescay H, Judge C, Lan L, Hajiyev P, Giger M, Gundeti M. Pilot study of machine learning in the task of distinguishing high and low-grade pediatric hydronephrosis on ultrasound. Investigative and Clinical Urology 2023;64(6):588 View
  3. Sriraam N, Chinta B, Suresh S, Sudharshan S. Ultrasound imaging based recognition of prenatal anomalies: a systematic clinical engineering review. Progress in Biomedical Engineering 2024;6(2):023002 View

Books/Policy Documents

  1. Kim D, Gam K, Gundeti M. Artificial Intelligence in Urology. View