Ensemble Learning of Multiple Models Using Deep Learning for Multiclass Classification of Ultrasound Images of Hepatic Masses

被引:8
|
作者
Nakata, Norio [1 ]
Siina, Tsuyoshi [2 ]
机构
[1] Jikei Univ, Ctr Integrated Med Res, Sch Med, Div Artificial Intelligence Med, 3-25-8 Nishi Shinbashi,Minato Ku, Tokyo 1058461, Japan
[2] Shibaura Inst Technol, Grad Sch Sci & Engn, 3-7-5 Toyosu,Koto Ku, Tokyo 1358548, Japan
来源
BIOENGINEERING-BASEL | 2023年 / 10卷 / 01期
关键词
ensemble learning; deep learning; convolutional neural network; liver; ultrasonography; artificial intelligence;
D O I
10.3390/bioengineering10010069
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Ultrasound (US) is often used to diagnose liver masses. Ensemble learning has recently been commonly used for image classification, but its detailed methods are not fully optimized. The purpose of this study is to investigate the usefulness and comparison of some ensemble learning and ensemble pruning techniques using multiple convolutional neural network (CNN) trained models for image classification of liver masses in US images. Dataset of the US images were classified into four categories: benign liver tumor (BLT) 6320 images, liver cyst (LCY) 2320 images, metastatic liver cancer (MLC) 9720 images, primary liver cancer (PLC) 7840 images. In this study, 250 test images were randomly selected for each class, for a total of 1000 images, and the remaining images were used as the training. 16 different CNNs were used for training and testing ultrasound images. The ensemble learning used soft voting (SV), weighted average voting (WAV), weighted hard voting (WHV) and stacking (ST). All four types of ensemble learning (SV, ST, WAV, and WHV) showed higher values of accuracy than the single CNN. All four types also showed significantly higher deep learning (DL) performance than ResNeXt101 alone. For image classification of liver masses using US images, ensemble learning improved the performance of DL over a single CNN.
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页数:20
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