TY - GEN
T1 - Artificial neural network in predicting cancer based on infrared spectroscopy
AU - Cohen, Yaniv
AU - Zilberman, Arkadi
AU - Dekel, Ben Zion
AU - Krouk, Evgenii
N1 - Publisher Copyright:
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2020.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - In this work, we present a Real-Time (RT), on-site, machine-learning-based methodology for identifying human cancers. The presented approach is reliable, effective, cost-effective, and non-invasive method, which is based on Fourier Transform Infrared (FTIR) spectroscopy—a vibrational method with the ability to detect changes as a result of molecular vibration bonds using Infrared (IR) radiation in human tissues and cells. Medical IR Optical System (IROS) is a tabletop device for real-time tissue diagnosis that utilizes FTIR spectroscopy and the Attenuated Total Reflectance (ATR) principle to accurately diagnose the tissue. The combined device and method were used for RT diagnosis and characterization of normal and pathological tissues ex vivo/in vitro. The solution methodology is to apply Machine Learning (ML) classifier that can be used to differentiate between cancer, normal, and other pathologies. Excellent results were achieved by applying feedforward backpropagation Artificial Neural Network (ANN) with supervised learning classification on 76 wet samples. ANN method shows a high performance to classify; overall, 98.7% (75/76 biopsies) of the predictions are correctly classified and 1.3% (1/76 biopsies) is wrong classification.
AB - In this work, we present a Real-Time (RT), on-site, machine-learning-based methodology for identifying human cancers. The presented approach is reliable, effective, cost-effective, and non-invasive method, which is based on Fourier Transform Infrared (FTIR) spectroscopy—a vibrational method with the ability to detect changes as a result of molecular vibration bonds using Infrared (IR) radiation in human tissues and cells. Medical IR Optical System (IROS) is a tabletop device for real-time tissue diagnosis that utilizes FTIR spectroscopy and the Attenuated Total Reflectance (ATR) principle to accurately diagnose the tissue. The combined device and method were used for RT diagnosis and characterization of normal and pathological tissues ex vivo/in vitro. The solution methodology is to apply Machine Learning (ML) classifier that can be used to differentiate between cancer, normal, and other pathologies. Excellent results were achieved by applying feedforward backpropagation Artificial Neural Network (ANN) with supervised learning classification on 76 wet samples. ANN method shows a high performance to classify; overall, 98.7% (75/76 biopsies) of the predictions are correctly classified and 1.3% (1/76 biopsies) is wrong classification.
KW - Artificial neural network
KW - Cancer
KW - Fourier transform
KW - Infrared spectroscopy attenuated total reflectance
UR - http://www.scopus.com/inward/record.url?scp=85086998726&partnerID=8YFLogxK
U2 - 10.1007/978-981-15-5925-9_12
DO - 10.1007/978-981-15-5925-9_12
M3 - Conference contribution
AN - SCOPUS:85086998726
SN - 9789811559242
T3 - Smart Innovation, Systems and Technologies
SP - 141
EP - 153
BT - Intelligent Decision Technologies - Proceedings of the 12th KES International Conference on Intelligent Decision Technologies, KES-IDT 2020
A2 - Czarnowski, Ireneusz
A2 - Howlett, Robert J.
A2 - Jain, Lakhmi C.
A2 - Jain, Lakhmi C.
A2 - Jain, Lakhmi C.
PB - Springer
T2 - 12th KES International Conference on Intelligent Decision Technologies, KES-IDT 2020
Y2 - 17 June 2020 through 19 June 2020
ER -
