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Research on the classification of lymphoma pathological images based on deep residual neural network

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 imag...

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Detalles Bibliográficos
Autores principales: Zhang, Xiaoli, Zhang, Kuixing, Jiang, Mei, Yang, Lin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IOS Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8150517/
https://www.ncbi.nlm.nih.gov/pubmed/33682770
http://dx.doi.org/10.3233/THC-218031
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author Zhang, Xiaoli
Zhang, Kuixing
Jiang, Mei
Yang, Lin
author_facet Zhang, Xiaoli
Zhang, Kuixing
Jiang, Mei
Yang, Lin
author_sort Zhang, Xiaoli
collection PubMed
description 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.
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spelling pubmed-81505172021-06-09 Research on the classification of lymphoma pathological images based on deep residual neural network Zhang, Xiaoli Zhang, Kuixing Jiang, Mei Yang, Lin Technol Health Care Research Article 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. IOS Press 2021-03-25 /pmc/articles/PMC8150517/ /pubmed/33682770 http://dx.doi.org/10.3233/THC-218031 Text en © 2021 – The authors. Published by IOS Press. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial (CC BY-NC 4.0) License (https://creativecommons.org/licenses/by-nc/4.0/) , which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Zhang, Xiaoli
Zhang, Kuixing
Jiang, Mei
Yang, Lin
Research on the classification of lymphoma pathological images based on deep residual neural network
title Research on the classification of lymphoma pathological images based on deep residual neural network
title_full Research on the classification of lymphoma pathological images based on deep residual neural network
title_fullStr Research on the classification of lymphoma pathological images based on deep residual neural network
title_full_unstemmed Research on the classification of lymphoma pathological images based on deep residual neural network
title_short Research on the classification of lymphoma pathological images based on deep residual neural network
title_sort research on the classification of lymphoma pathological images based on deep residual neural network
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8150517/
https://www.ncbi.nlm.nih.gov/pubmed/33682770
http://dx.doi.org/10.3233/THC-218031
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