Abstract
Endoscopic ultrasonography (EUS) has been found to be of great advantage in the diagnosis
of digestive tract submucosal tumors. However, EUS-based diagnosis is limited by variability
in subjective interpretation on the part of doctors. Tumor classification of ultrasound
images with the computer-aided diagnosis system can significantly improve the diagnostic
efficiency and accuracy of doctors. In this study, we proposed a multifeature fusion
classification method for adaptive EUS tumor images. First, for different ultrasound
tumor images, we selected the region of interest based on prior information to facilitate
the estimation in the subsequent works. Second, we proposed a method based on image
gray histogram feature extraction with principal component analysis dimensionality
reduction, which learns the gray distribution of different tumor images effectively.
Third, we fused the reduced grayscale features with the improved local binary pattern
features and gray-level co-occurrence matrix features, and then used the multiclassification
support vector machine. Finally, in the experiment, we selected the 431 ultrasound
images of 109 patients in the hospital and compared the experimental effects of different
features and different classifiers. The results revealed that the proposed method
performed best, with the highest accuracy of 96.18% and an area under the curve of
99%. It is evident that the method proposed in this study can efficiently contribute
to the classification of EUS tumor images.
Key Words
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Article info
Publication history
Published online: January 19, 2023
Accepted:
November 6,
2022
Received in revised form:
October 31,
2022
Received:
July 5,
2022
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2022 World Federation for Ultrasound in Medicine & Biology. All rights reserved.
