TY - JOUR
T1 - An IoMT-Based Federated and Deep Transfer Learning Approach to the Detection of Diverse Chest Diseases Using Chest X-Rays
AU - Kakkar, Barkha
AU - Johri, Prashant
AU - Kumar, Yogesh
AU - Park, Hyunwoo
AU - Son, Youngdoo
AU - Shafi, Jana
N1 - Publisher Copyright:
© 2022. Human-centric Computing and Information Sciences. All Rights Reserved.
PY - 2022
Y1 - 2022
N2 - Since chest illnesses are so frequent these days, it is critical to identify and diagnose them effectively. As such, this study proposes a model designed to accurately predict chest disorders by analyzing multiple chest x-ray pictures obtained from a dataset, consisting of 112,120 chest X-ray images, obtained the National Institute of Health (NIH) X-ray. The study used photos from 30,805 individuals with a total of 14 different types of chest disorder, including atelectasis, consolidation, infiltration, and pneumothorax, as well as a class called “No findings” for cases in which the ailment was undiagnosed. Six distinct transfer-learning approaches, namely, VGG-16, MobileNet V2, ResNet-50, DenseNet-161, Inception V3, and VGG-19, were used in the deep learning and federated learning environment to predict the accuracy rate of detecting chest disorders. The VGG-16 model showed the best accuracy at 0.81, with a recall rate of 0.90. As a result, the F1 score of VGG-16 is 0.85, which was higher than the F1 scores computed by other transfer learning approaches. VGG-19 obtained a maximum rate of accuracy of 97.71% via federated transfer learning. According to the classification report, the VGG-16 model is the best transfer-learning model for correctly detecting chest illness.
AB - Since chest illnesses are so frequent these days, it is critical to identify and diagnose them effectively. As such, this study proposes a model designed to accurately predict chest disorders by analyzing multiple chest x-ray pictures obtained from a dataset, consisting of 112,120 chest X-ray images, obtained the National Institute of Health (NIH) X-ray. The study used photos from 30,805 individuals with a total of 14 different types of chest disorder, including atelectasis, consolidation, infiltration, and pneumothorax, as well as a class called “No findings” for cases in which the ailment was undiagnosed. Six distinct transfer-learning approaches, namely, VGG-16, MobileNet V2, ResNet-50, DenseNet-161, Inception V3, and VGG-19, were used in the deep learning and federated learning environment to predict the accuracy rate of detecting chest disorders. The VGG-16 model showed the best accuracy at 0.81, with a recall rate of 0.90. As a result, the F1 score of VGG-16 is 0.85, which was higher than the F1 scores computed by other transfer learning approaches. VGG-19 obtained a maximum rate of accuracy of 97.71% via federated transfer learning. According to the classification report, the VGG-16 model is the best transfer-learning model for correctly detecting chest illness.
KW - Chest diseases
KW - Deep learning
KW - Disease prediction
KW - Federated learning
KW - X-ray dataset
UR - http://www.scopus.com/inward/record.url?scp=85131431899&partnerID=8YFLogxK
U2 - 10.22967/HCIS.2022.12.024
DO - 10.22967/HCIS.2022.12.024
M3 - Article
AN - SCOPUS:85131431899
SN - 2192-1962
VL - 12
JO - Human-centric Computing and Information Sciences
JF - Human-centric Computing and Information Sciences
M1 - 24
ER -