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Research on Application of Classification Model Based on Stack Generalization in Staging of Cervical Tissue Pathological Images
- Source :
- IEEE Access, Vol 9, Pp 48980-48991 (2021)
- Publication Year :
- 2021
- Publisher :
- IEEE, 2021.
-
Abstract
- Cervical cancer is a malignant tumor that threatens women’s health and life. Cervical pathology examination, as the gold standard for cervical cancer diagnosis, provides an important basis for the surgical plan and postoperative follow-up strategy for cervical cancer. Cervical biopsy diagnosis includes normal, low-grade squamous intraepithelial lesion (LSIL), high-grade squamous intraepithelial lesion (HSIL) and squamous cell carcinoma (SCC). At present, cervical pathology examination still relies on the doctor’s personal clinical experience and subjective judgment, which is time-consuming and may cause misdiagnosis or missed diagnosis. In addition, the current intelligent classification of cervical pathological images still has disadvantages such as imperfect classification system and low classification accuracy. Therefore, this experiment uses the ResNet50 model of the convolutional neural network as the feature extractor, and selects the K-Nearest Neighbour (KNN), Random Forest (RF), Support Vector Machine (SVM) classifiers in Machine Learning to perform cervical tissue pathological images Discrimination, the accuracy of the classification results were 85.83%, 80.33%, and 86.67%. In order to further improve the accuracy of the model and enhance the applicability and stability of the model, this experiment proposes the Stacked Generalization (SK) classification model. The first-layer base learner of the SK classification model selects CNN-KNN, CNN-RF, CNN-SVM, and the second-layer classifier selects Multilayer Perceptron (MLP). Among them, MLP makes the final result by learning the classification performance of the base learner for label discrimination, the accuracy of the classification model after ensemble learning is 90.00%. In addition, this experiment uses the Synthetic Minority Oversampling Technique (SMOTE) algorithm to amplify the training samples, and the amplified data set has a classification accuracy of 89.17% under the training of the SK classification model. The results show that the SK classification model in this experiment has a high classification ability for cervical histopathological images, and has good generalization ability and robustness.
- Subjects :
- General Computer Science
stacked generalization
Computer science
Feature extraction
0211 other engineering and technologies
02 engineering and technology
0202 electrical engineering, electronic engineering, information engineering
medicine
General Materials Science
multilayer perceptron
SMOTE
021101 geological & geomatics engineering
Cervical cancer
business.industry
General Engineering
Pattern recognition
medicine.disease
Ensemble learning
Random forest
Cervical pathology image
Support vector machine
Statistical classification
machine learning
Feature (computer vision)
Multilayer perceptron
020201 artificial intelligence & image processing
Artificial intelligence
lcsh:Electrical engineering. Electronics. Nuclear engineering
business
lcsh:TK1-9971
ResNet50
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 9
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....3644bdeb9e3af1509d463ca88c1892b8