Kwon Ickhwan, Wang Soo-Geun, Shin Sung-Chan, Cheon Yong-Il, Lee Byung-Joo, Lee Jin-Choon, Lim Dong-Won, Jo Cheolwoo, Cho Youngseuk, Shin Bum-Joo
Department of Applied IT and Engineering, Pusan National University, Miryang, Gyeongsangnam-do, South Korea.
Department of Otorhinolaryngology-Head and Neck Surgery, College of Medicine, Pusan National University and Medical Research Institute, Pusan National University Hospital, Busan, South Korea.
J Voice. 2025 Jan;39(1):245-257. doi: 10.1016/j.jvoice.2022.07.007. Epub 2022 Sep 6.
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.
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.
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 %.
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.
本研究的目的是通过比较应用决策树集成学习（这是提高相对较小数据集分类准确率的方法之一）所获得的结果与卷积神经网络（CNN）算法用于声门癌诊断所获得的结果，来提高分类准确率。
使用釜山国立大学医院（PNUH）数据集建立分类器，并使用釜山国立大学梁山医院（PNUYH）数据集验证生成模型中分类器的性能。对于声门癌的诊断，使用基于深度学习的CNN模型，并利用喉部图像和语音数据进行分类。通过使用CNN分类算法的概率执行决策树集成学习来获得分类准确率。在此过程中，使用了分类与回归树（CART）方法。然后，通过将喉部图像与语音决策树分类器融合，比较决策树集成学习与CNN单个分类器的分类准确率。
在使用PNUH训练数据集建立的喉部图像和语音分类模型中，我们分别获得了81.03%和99.18%的分类准确率。然而，当使用PNUYH的外部数据集时，CNN分类器的分类准确率在语音方面降至73.88%，在喉部图像方面降至68.92%。为了解决这个问题，使用了喉部图像和语音的决策树集成学习，并通过整合同一人的喉部图像和语音数据提高了分类准确率。个性化喉部图像和语音决策树模型的分类准确率分别为87.88%和89.06%，喉部图像和语音决策树结果的融合代表分类准确率为95.31%。
我们的研究结果表明，旨在训练多个分类器的决策树集成学习对于在数据集较小的情况下提高分类准确率是有用的。尽管大量数据对于人工智能分析至关重要，但当采用综合方法结合各种输入数据时，可以预期获得较高的诊断分类准确率。
