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

Machine Learning (1449) Imaging Informatics (192) Artificial Intelligence (1351) New Methods (941) mHealth for Treatment Adherence (397)

Published on in Vol 9, No 7 (2021): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26000, first published .
Effects of Background Colors, Flashes, and Exposure Values on the Accuracy of a Smartphone-Based Pill Recognition System Using a Deep Convolutional Neural Network: Deep Learning and Experimental Approach

Effects of Background Colors, Flashes, and Exposure Values on the Accuracy of a Smartphone-Based Pill Recognition System Using a Deep Convolutional Neural Network: Deep Learning and Experimental Approach

Effects of Background Colors, Flashes, and Exposure Values on the Accuracy of a Smartphone-Based Pill Recognition System Using a Deep Convolutional Neural Network: Deep Learning and Experimental Approach

Authors of this article:

KyeongMin Cha1 Author Orcid Image ;   Hyun-Ki Woo1, 2 Author Orcid Image ;   Dohyun Park1 Author Orcid Image ;   Dong Kyung Chang1, 3 Author Orcid Image ;   Mira Kang1, 4 Author Orcid Image
Article Authors Cited by (4) Tweetations (2) Metrics

KyeongMin Cha   1 , MSc ;   Hyun-Ki Woo   1, 2 , MSc ;   Dohyun Park   1 , MSc ;   Dong Kyung Chang   1, 3 , MD, PhD ;   Mira Kang   1, 4 , MD, PhD

1 Department of Digital Health, Samsung Advanced Institute of Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea

2 EvidNet Inc, Seongnam-si, Gyeonggi-do, Republic of Korea

3 Division of Gastroenterology, Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

4 Center for Health Promotion, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

Corresponding Author:

  • Mira Kang, MD, PhD
  • Department of Digital Health
  • Samsung Advanced Institute of Health Sciences & Technology
  • Sungkyunkwan University
  • 81 Irwon-ro, Gangnam-gu
  • Seoul, 06351
  • Republic of Korea
  • Phone: 82 01099336838
  • Fax: 82 0234101000
  • Email: kang.mirad@gmail.com