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Research on the classification of lymphoma pathological images based on deep residual neural network
- Source :
- Technology and Health Care
- Publication Year :
- 2021
- Publisher :
- IOS Press, 2021.
-
Abstract
- BACKGROUND: Malignant lymphoma is a type of tumor that originated from the lymphohematopoietic system, with complex etiology, diverse pathological morphology, and classification. It takes a lot of time and energy for doctors to accurately determine the type of lymphoma by observing pathological images. OBJECTIVE: At present, an automatic classification technology is urgently needed to assist doctors in analyzing the type of lymphoma. METHODS: In this paper, by comparing the training results of the BP neural network and BP neural network optimized by genetic algorithm (GA-BP), adopts a deep residual neural network model (ResNet-50), with 374 lymphoma pathology images as the experimental data set. After preprocessing the dataset by image flipping, color transformation, and other data enhancement methods, the data set is input into the ResNet-50 network model, and finally classified by the softmax layer. RESULTS: The training results showed that the classification accuracy was 98.63%. By comparing the classification effect of GA-BP and BP neural network, the accuracy of the network model proposed in this paper is improved. CONCLUSIONS: The network model can provide an objective basis for doctors to diagnose lymphoma types.
- Subjects :
- Lymphoma
Computer science
Biomedical Engineering
Biophysics
Health Informatics
Bioengineering
automatic classification
Biomaterials
Set (abstract data type)
03 medical and health sciences
0302 clinical medicine
Physicians
Genetic algorithm
Humans
Preprocessor
pathological images
030304 developmental biology
Network model
0303 health sciences
Artificial neural network
business.industry
Deep learning
deep learning
Pattern recognition
Data set
030220 oncology & carcinogenesis
Softmax function
Disease Progression
Neural Networks, Computer
Artificial intelligence
business
Resnet-50
Research Article
Information Systems
Subjects
Details
- ISSN :
- 18787401 and 09287329
- Volume :
- 29
- Database :
- OpenAIRE
- Journal :
- Technology and Health Care
- Accession number :
- edsair.doi.dedup.....fa06b20b114a794668b258cd77defb67
- Full Text :
- https://doi.org/10.3233/thc-218031