Research on breast cancer pathological image classification method based on wavelet transform and YOLOv8.
J Xray Sci Technol
; 32(3): 677-687, 2024.
Article
in En
| MEDLINE
| ID: mdl-38189740
ABSTRACT
Breast cancer is one of the cancers with high morbidity and mortality in the world, which is a serious threat to the health of women. With the development of deep learning, the recognition about computer-aided diagnosis technology is getting higher and higher. And the traditional data feature extraction technology has been gradually replaced by the feature extraction technology based on convolutional neural network which helps to realize the automatic recognition and classification of pathological images. In this paper, a novel method based on deep learning and wavelet transform is proposed to classify the pathological images of breast cancer. Firstly, the image flip technique is used to expand the data set, then the two-level wavelet decomposition and reconfiguration technology is used to sharpen and enhance the pathological images. Secondly, the processed data set is divided into the training set and the test set according to 82 and 73, and the YOLOv8 network model is selected to perform the eight classification tasks of breast cancer pathological images. Finally, the classification accuracy of the proposed method is compared with the classification accuracy obtained by YOLOv8 for the original BreaKHis dataset, and it is found that the algorithm can improve the classification accuracy of images with different magnifications, which proves the effectiveness of combining two-level wavelet decomposition and reconfiguration with YOLOv8 network model.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Algorithms
/
Breast Neoplasms
/
Neural Networks, Computer
/
Wavelet Analysis
Limits:
Female
/
Humans
Language:
En
Journal:
J Xray Sci Technol
/
J. X-ray sci. technol
/
Journal of x-ray science and technology
Journal subject:
RADIOLOGIA
Year:
2024
Document type:
Article
Affiliation country:
China
Country of publication:
Países Bajos