TY - JOUR
T1 - A multi-channel deep convolutional neural network for multi-classifying thyroid diseases
AU - Zhang, Xinyu
AU - Lee, Vincent C.S.
AU - Rong, Jia
AU - Lee, James C.
AU - Song, Jiangning
AU - Liu, Feng
N1 - Funding Information:
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. All authors approved the version of the manuscript to be published.
Publisher Copyright:
© 2022
PY - 2022/9
Y1 - 2022/9
N2 - Background and Objective: Thyroid disease instances have been continuously increasing since the 1990s, and thyroid cancer has become the most rapidly rising disease among all the malignancies in recent years. Most existing studies focused on applying deep convolutional neural networks for detecting thyroid cancer. Despite their satisfactory performance on binary classification tasks, limited studies have explored multi-class classification of thyroid disease types; much less is known of the diagnosis of co-existence situation for different types of thyroid diseases. Method: This study proposed a novel multi-channel convolutional neural network (CNN) architecture to address the multi-class classification task of thyroid disease. The multi-channel CNN merits from computed tomography characteristics to drive a comprehensive diagnostic decision for the overall thyroid gland, emphasizing the disease co-existence circumstance. Moreover, this study also examined alternative strategies to enhance the diagnostic accuracy of CNN models through concatenation of different scales of feature maps. Results: Benchmarking experiments demonstrate the improved performance of the proposed multi-channel CNN architecture compared with the standard single-channel CNN architecture. More specifically, the multi-channel CNN achieved an accuracy of 0.909±0.048, precision of 0.944±0.062, recall of 0.896±0.047, specificity of 0.994±0.001, and F1 of 0.917±0.057, in contrast to the single-channel CNN, which obtained 0.902±0.004, 0.892±0.005, 0.909±0.002, 0.993±0.001, 0.898±0.003, respectively. In addition, the proposed model was evaluated in different gender groups; it reached a diagnostic accuracy of 0.908 for the female group and 0.901 for the male group. Conclusion: Collectively, the results highlight that the proposed multi-channel CNN has excellent generalization and has the potential to be deployed to provide computational decision support in clinical settings.
AB - Background and Objective: Thyroid disease instances have been continuously increasing since the 1990s, and thyroid cancer has become the most rapidly rising disease among all the malignancies in recent years. Most existing studies focused on applying deep convolutional neural networks for detecting thyroid cancer. Despite their satisfactory performance on binary classification tasks, limited studies have explored multi-class classification of thyroid disease types; much less is known of the diagnosis of co-existence situation for different types of thyroid diseases. Method: This study proposed a novel multi-channel convolutional neural network (CNN) architecture to address the multi-class classification task of thyroid disease. The multi-channel CNN merits from computed tomography characteristics to drive a comprehensive diagnostic decision for the overall thyroid gland, emphasizing the disease co-existence circumstance. Moreover, this study also examined alternative strategies to enhance the diagnostic accuracy of CNN models through concatenation of different scales of feature maps. Results: Benchmarking experiments demonstrate the improved performance of the proposed multi-channel CNN architecture compared with the standard single-channel CNN architecture. More specifically, the multi-channel CNN achieved an accuracy of 0.909±0.048, precision of 0.944±0.062, recall of 0.896±0.047, specificity of 0.994±0.001, and F1 of 0.917±0.057, in contrast to the single-channel CNN, which obtained 0.902±0.004, 0.892±0.005, 0.909±0.002, 0.993±0.001, 0.898±0.003, respectively. In addition, the proposed model was evaluated in different gender groups; it reached a diagnostic accuracy of 0.908 for the female group and 0.901 for the male group. Conclusion: Collectively, the results highlight that the proposed multi-channel CNN has excellent generalization and has the potential to be deployed to provide computational decision support in clinical settings.
KW - Computer-aided diagnosis (CAD)
KW - Convolutional neural network (CNN)
KW - Deep learning
KW - Multi-channel CNN
KW - Multi-class classification
KW - Thyroid disease diagnosis
UR - http://www.scopus.com/inward/record.url?scp=85135886128&partnerID=8YFLogxK
U2 - 10.1016/j.compbiomed.2022.105961
DO - 10.1016/j.compbiomed.2022.105961
M3 - Article
C2 - 35985185
AN - SCOPUS:85135886128
SN - 0010-4825
VL - 148
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
M1 - 105961
ER -
