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20 results on '"Ma, Xuesi"'

1. An Improved k‐Nearest Neighbor Algorithm for Recognition and Classification of Thyroid Nodules.

Author

Ma, Xuesi, Han, Xiang, and Zhang, Lina

Subjects
K-nearest neighbor classification, THYROID nodules, CLASSIFICATION algorithms, MACHINE learning, AUTOMATIC classification
Abstract

Objectives: To complete the task of automatic recognition and classification of thyroid nodules and solve the problem of high classification error rates when the samples are imbalanced. Methods: An improved k‐nearest neighbor (KNN) algorithm is proposed and a method for automatic thyroid nodule classification based on the improved KNN algorithm is established. In the improved KNN algorithm, we consider not only the number of class labels for various classes of data in KNNs, but also the corresponding weights. And we use the Minkowski distance measure instead of the Euclidean distance measure. Results: A total of 508 ultrasound images of thyroid nodules, including 415 benign nodules and 93 malignant nodules, were used in the paper. Experimental results show the improved KNN has 0.872549 accuracy, 0.867347 precision, 1 recall, and 0.928962 F1‐score. At the same time, we also considered the influence of different distance weights, the value of k, different distance measures on the classification results. Conclusions: A comparison result shows that our method has a better performance than the traditional KNN and other classical machine learning methods. [ABSTRACT FROM AUTHOR]

Published
2024
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6. A Machine Learning-based Diagnosis of Thyroid Cancer Using Thyroid Nodules Ultrasound Images

Author

Yang Jialiang, Ma Xuesi, Sui Xin, Tian Geng, Zhu Lijuan, Zhang Yi, and Xi Baohang

Subjects
0301 basic medicine, Thyroid nodules, medicine.medical_specialty, business.industry, Ultrasound, medicine.disease, Biochemistry, 03 medical and health sciences, Computational Mathematics, 030104 developmental biology, 0302 clinical medicine, 030220 oncology & carcinogenesis, Genetics, medicine, Radiology, business, Molecular Biology, Thyroid cancer
Abstract

Background:: Ultrasound test is one of the routine tests for the diagnosis of thyroid cancer. The diagnosis accuracy depends largely on the correct interpretation of ultrasound images of thyroid nodules. However, human eye-based image recognition is usually subjective and sometimes error-prone especially for less experienced doctors, which presents a need for computeraided diagnostic systems. Objective: : To our best knowledge, there is no well-maintained ultrasound image database for the Chinese population. In addition, though there are several computational methods for image-based thyroid cancer detection, a comparison among them is missing. Finally, the effects of features like the choice of distance measures have not been assessed. The study aims to give the improvement of these limitations and proposes a highly accurate image-based thyroid cancer diagnosis system, which can better assist doctors in the diagnosis of thyroid cancer. Methods:: We first establish a novel thyroid nodule ultrasound image database consisting of 508 images collected from the Third Hospital of Hebei Medical University in China. The clinical information for the patients is also collected from the hospital, where 415 patients are diagnosed to be benign and 93 are malignant by doctors following a standard diagnosis procedure. We develop and apply five machine learning methods to the dataset including deep neural network, support vector machine, the center clustering method, k-nearest neighbor, and logistic regression. Results:: Experimental results show that deep neural network outperforms other diagnosis methods with an average cross-validation accuracy of 0.87 in 10 runs. Meanwhile, we also explore the performance of four image distance measures including the Euclidean distance, the Manhattan distance, the Chebyshev distance, and the Minkowski distance, among which the Chebyshev distance is the best. The resource can be directly used to aid doctors in thyroid cancer diagnosis and treatment. Conclusions: : The paper establishes a novel thyroid nodule ultrasound image database and develops a high accurate image-based thyroid cancer diagnosis system which can better assist doctors in the diagnosis of thyroid cancer.

Published
2020

14. Subgroup‐adaptive randomization for subgroup confirmation in clinical trials.

Author

Liu, Zhongqiang, Ma, Xuesi, and Wang, Zhaoliang

Abstract

A well‐known issue when testing for treatment‐by‐subgroup interaction is its low power, as clinical trials are generally powered for establishing efficacy claims for the overall population, and they are usually not adequately powered for detecting interaction (Alosh, Huque, & Koch [2015] Journal of Biopharmaceutical Statistics, 25, 1161–1178). Hence, it is necessary to develop an adaptive design to improve the efficiency of detecting heterogeneous treatment effects within subgroups. Considering Neyman allocation can maximize the power of usual Z‐test (see p. 194 of the book edited by Rosenberger and Lachin), we propose a subgroup‐adaptive randomization procedure aiming to achieve Neyman allocation in both predefined subgroups and overall study population in this paper. To verify whether the proposed randomization procedure works as intended, relevant theoretical results are derived and displayed. Numerical studies show that the proposed randomization procedure has obvious advantages in power of tests compared with complete randomization and Pocock and Simon's minimization method. [ABSTRACT FROM AUTHOR]

Published
2021
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