Summary
Objectives
The purpose of study is to improve the classification accuracy by comparing the results
obtained by applying decision tree ensemble learning, which is one of the methods
to increase the classification accuracy for a relatively small dataset, with the results
obtained by the convolutional neural network (CNN) algorithm for the diagnosis of
glottal cancer.
Methods
Pusan National University Hospital (PNUH) dataset were used to establish classifiers
and Pusan National University Yangsan Hospital (PNUYH) dataset were used to verify
the classifier's performance in the generated model. For the diagnosis of glottic
cancer, deep learning-based CNN models were established and classified using laryngeal
image and voice data. Classification accuracy was obtained by performing decision
tree ensemble learning using probability through CNN classification algorithm. In
this process, the classification and regression tree (CART) method was used. Then,
we compared the classification accuracy of decision tree ensemble learning with CNN
individual classifiers by fusing the laryngeal image with the voice decision tree
classifier.
Results
We obtained classification accuracy of 81.03 % and 99.18 % in the established laryngeal
image and voice classification models using PNUH training dataset, respectively. However,
the classification accuracy of CNN classifiers decreased to 73.88 % in voice and 68.92
% in laryngeal image when using an external dataset of PNUYH. To solve this problem,
decision tree ensemble learning of laryngeal image and voice was used, and the classification
accuracy was improved by integrating data of laryngeal image and voice of the same
person. The classification accuracy was 87.88 % and 89.06 % for the individualized
laryngeal image and voice decision tree model respectively, and the fusion of the
laryngeal image and voice decision tree results represented a classification accuracy
of 95.31 %.
Conclusion
The results of our study suggest that decision tree ensemble learning aimed at training
multiple classifiers is useful to obtain an increased classification accuracy despite
a small dataset. Although a large data amount is essential for AI analysis, when an
integrated approach is taken by combining various input data high diagnostic classification
accuracy can be expected.
Key Words
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Article info
Publication history
Published online: September 05, 2022
Accepted:
July 6,
2022
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2022 The Voice Foundation. Published by Elsevier Inc. All rights reserved.
