TY - GEN
T1 - AI-Based Classification Algorithm of Infrared Images of Patients with Spinal Disorders
AU - Poplavska, Anna
AU - Vassilenko, Valentina
AU - Poplavskyi, Oleksandr
AU - Casal, Diogo
N1 - Funding Information:
info:eu-repo/grantAgreement/FCT/OE/PD%2FBDE%2F142791%2F2018/PT#
The authors would like to express their deepest gratitude to all volunteers and medical staff from the Center of Medical Rehabilitation & Sports Medicine, Vinnytsia, Ukraine, for participating in the study. Special thanks to Professor Petro F. Kolisnyk and Professor Sergei P. Kolisnyk from National Pirogov Memorial Medical University for assistance, and Professor Dr. Sergei V. Pavlov from Vinnytsia National Technical University for help in research work.
Publisher Copyright:
© 2021, IFIP International Federation for Information Processing.
PY - 2021
Y1 - 2021
N2 - Infrared thermal imaging is a non-destructive, non-invasive technique that has shown to be effective in the detection and pre-clinical diagnosis of a variety of disorders. Nowadays, some medical applications have already been successfully implemented in pre-clinic diagnostics using thermography based on AI algorithms to support decision-based medical tasks. Though, the massive amount of image types, disease variety, and numerous individual anatomical features of the human body continue to give researchers more challenging jobs that still need to be solved. This paper proposes a novel methodology using a convolutional neural network (CNN) for analyzing with high accuracy infrared thermal images from the spine region for quick screening and disease classification of patients.
AB - Infrared thermal imaging is a non-destructive, non-invasive technique that has shown to be effective in the detection and pre-clinical diagnosis of a variety of disorders. Nowadays, some medical applications have already been successfully implemented in pre-clinic diagnostics using thermography based on AI algorithms to support decision-based medical tasks. Though, the massive amount of image types, disease variety, and numerous individual anatomical features of the human body continue to give researchers more challenging jobs that still need to be solved. This paper proposes a novel methodology using a convolutional neural network (CNN) for analyzing with high accuracy infrared thermal images from the spine region for quick screening and disease classification of patients.
KW - AI decision making
KW - Biomedical applications
KW - Convolutional neural network
KW - Medical thermography
KW - Spinal disorders
UR - http://www.scopus.com/inward/record.url?scp=85111970421&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-78288-7_30
DO - 10.1007/978-3-030-78288-7_30
M3 - Conference contribution
AN - SCOPUS:85111970421
SN - 978-3-030-78287-0
T3 - IFIP Advances in Information and Communication Technology
SP - 316
EP - 323
BT - Technological Innovation for Applied AI Systems - 12th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2021, Proceedings
A2 - Camarinha-Matos, Luis M.
A2 - Ferreira, Pedro
A2 - Brito, Guilherme
PB - Springer
CY - Cham
T2 - 12th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2021
Y2 - 7 July 2021 through 9 July 2021
ER -
