ArticleScientific reports2024
A novel breast cancer image classification model based on multiscale texture feature analysis and dynamic learning.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- AI-Driven Breast Cancer Nuclei Segmentation, Classification, and Scoring in PR-IHC Images.Diagnostics (Basel, Switzerland) · 2026Article
- Article
- Retrospection dropout bare-bones particle swarm optimization for feature-based brain tumor classification in MRI images.Frontiers in oncology · 2026Article
- An effective microscopic image augmentation approach.Scientific reports · 2025Article
- A cosine adaptive particle swarm optimization based long-short term memory method for urban green area prediction.PeerJ. Computer science · 2024Article
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7 authors.
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Abstract
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.
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