Fx-Net and PureNet: Convolutional Neural Network architecture for discrimination of Chronic Obstructive Pulmonary Disease from smokers and healthy subjects through electronic nose signals

• Published in: Computers in biology and medicine Vol. 148; p. 105913
• Main Authors: Avian, Cries, Mahali, Muhammad Izzuddin, Putro, Nur Achmad Sulistyo, Prakosa, Setya Widyawan, Leu, Jenq-Shiou
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.09.2022; Elsevier Limited
• Subjects: Artificial neural networks; Chronic obstructive pulmonary disease; Chronic obstructive pulmonary disease (COPD); Classifiers; Computer architecture; Convolutional neural network; Electronic nose; Electronic noses; Feature extraction; Internal Medicine; Kernel principal component analysis; Lung cancer; Lung diseases; Machine learning; Mortality; Neural networks; Obstructive lung disease; Other; Principal components analysis; Sensors; Signal processing; Smoking; Standard deviation; VOCs; Volatile organic compounds; Chronic obstructive pulmonary disease (COPD); Kernel principal component analysis; Convolutional neural network; Lung cancer; Electronic nose

As one of the most reliable and significant indicators, Chronic Obstructive Pulmonary Disease (COPD) becomes a robust predictor of lung cancer early detection, the world's leading cause of cancer death. One of the methods is to analyze the Volatile Organic Compounds (VOCs) in exhaled breath usi...