A novel breast cancer image classification model based on multiscale texture feature analysis and dynamic learning

Assistive 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 ap...

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Bibliographic Details
Published in:Scientific reports 2024-03, Vol.14 (1), p.7216-7216, Article 7216
Main Authors: Guo, Jia, Yuan, Hao, Shi, Binghua, Zheng, Xiaofeng, Zhang, Ziteng, Li, Hongyan, Sato, Yuji
Format: Article
Language:English
Subjects:
631/67/1347
639/624/1075/146
Accuracy
Algorithms
Artificial intelligence
Breast cancer
Breast cancer image classification
Breast Neoplasms - diagnostic imaging
Classification
Computer Simulation
Datasets
Deep learning
Dynamic learning strategy
Evolution & development
Expression vectors
Female
Graph representations
Humanities and Social Sciences
Humans
Machine Learning
Mammography
Medical diagnosis
Medical personnel
Medical prognosis
Medical research
Multi-scale texture analysis
multidisciplinary
Neural networks
Research methodology
Science
Science (multidisciplinary)
Working conditions
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Summary:Assistive 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.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-57891-5