A novel breast cancer image classification model based on multiscale texture feature analysis and dynamic learning.
Sci Rep
; 14(1): 7216, 2024 03 27.
Article
in En
| MEDLINE
| ID: mdl-38538814
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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Breast Neoplasms
Limits:
Female
/
Humans
Language:
En
Journal:
Sci Rep
Year:
2024
Document type:
Article
Affiliation country:
China
Country of publication:
Reino Unido