TY - JOUR
T1 - A Detailed Study on the Choice of Hyperparameters for Transfer Learning in Covid-19 Image Datasets using Bayesian Optimization
AU - Miranda, Miguel
AU - Valeriano, Kid
AU - Sulla Torres, Jose Alfredo
N1 - Publisher Copyright:
© 2021
PY - 2021
Y1 - 2021
N2 - For many years, the area of health care has evolved, mainly using medical images to detect and evaluate diseases. Nowadays, the world is going through a pandemic due to COVID-19, causing a severe effect on the health system and the global economy. Researchers, both in health and in different areas, are focused on improving and providing various alternatives for rapid and more effective detection of this disease. The main objective of this study is to automatically explore as many configurations as possible to recommend a smaller starting hyperparameter space. Because the manual selection of these hyperparameters can lose configurations that generate more efficient models, for this, we present the MKCovid-19 workflow, which uses chest x-ray images of patients with COVID-19. We use knowledge transfer based on convolutional neural networks and Bayes optimization. A detailed study was conducted with different amounts of training data. This automatic selection of hyperparameters allowed us to find a robust model with an accuracy of 98% in test data.
AB - For many years, the area of health care has evolved, mainly using medical images to detect and evaluate diseases. Nowadays, the world is going through a pandemic due to COVID-19, causing a severe effect on the health system and the global economy. Researchers, both in health and in different areas, are focused on improving and providing various alternatives for rapid and more effective detection of this disease. The main objective of this study is to automatically explore as many configurations as possible to recommend a smaller starting hyperparameter space. Because the manual selection of these hyperparameters can lose configurations that generate more efficient models, for this, we present the MKCovid-19 workflow, which uses chest x-ray images of patients with COVID-19. We use knowledge transfer based on convolutional neural networks and Bayes optimization. A detailed study was conducted with different amounts of training data. This automatic selection of hyperparameters allowed us to find a robust model with an accuracy of 98% in test data.
KW - Bayes optimization
KW - COVID-19
KW - deep learning
KW - hyperparameter optimization
KW - machine learning
KW - Transfer Learning
KW - X-ray image
UR - http://www.scopus.com/inward/record.url?scp=85105788071&partnerID=8YFLogxK
U2 - 10.14569/IJACSA.2021.0120441
DO - 10.14569/IJACSA.2021.0120441
M3 - Artículo
AN - SCOPUS:85105788071
VL - 12
SP - 327
EP - 335
JO - International Journal of Advanced Computer Science and Applications
JF - International Journal of Advanced Computer Science and Applications
SN - 2158-107X
IS - 4
ER -