Author:
Guo Jia,Yuan Hao,Shi Binghua,Zheng Xiaofeng,Zhang Ziteng,Li Hongyan,Sato Yuji
Abstract
AbstractAssistive medical image classifiers can greatly reduce the workload of medical personnel. However, traditional machine learning methods require large amounts of well-labeled data and long learning times to solve medical image classification problems, which can lead to high training costs and poor applicability. To address this problem, a novel unsupervised breast cancer image classification model based on multiscale texture analysis and a dynamic learning strategy for mammograms is proposed in this paper. First, a gray-level cooccurrence matrix and Tamura coarseness are used to transfer images to multiscale texture feature vectors. Then, an unsupervised dynamic learning mechanism is used to classify these vectors. In the simulation experiments with a resolution of 40 pixels, the accuracy, precision, F1-score and AUC of the proposed method reach 91.500%, 92.780%, 91.370%, and 91.500%, respectively. The experimental results show that the proposed method can provide an effective reference for breast cancer diagnosis.
Funder
Natural Science Foundation of China
Natural Science Foundation of Hubei Province
Hubei Provincial Education Department Scientific Research Program Project
Natural Science Foundation of Hubei Province
Education Department Scientific Research Program Project of Hubei Province of China
Hubei University of Economics Research and Cultivation Key Project
JSPS KAKENHI Grant
Publisher
Springer Science and Business Media LLC
Cited by
1 articles.
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