Classification of multi-differentiated liver cancer pathological images based on deep learning attention mechanism
Abstract Purpose Liver cancer is one of the most common malignant tumors in the world, ranking fifth in malignant tumors. The degree of differentiation can reflect the degree of malignancy. The degree of malignancy of liver cancer can be divided into three types: poorly differentiated, moderately di...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-07-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-022-01919-1 |
| _version_ | 1818158437998002176 |
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| author | Chen Chen Cheng Chen Mingrui Ma Xiaojian Ma Xiaoyi Lv Xiaogang Dong Ziwei Yan Min Zhu Jiajia Chen |
| author_facet | Chen Chen Cheng Chen Mingrui Ma Xiaojian Ma Xiaoyi Lv Xiaogang Dong Ziwei Yan Min Zhu Jiajia Chen |
| author_sort | Chen Chen |
| collection | DOAJ |
| description | Abstract Purpose Liver cancer is one of the most common malignant tumors in the world, ranking fifth in malignant tumors. The degree of differentiation can reflect the degree of malignancy. The degree of malignancy of liver cancer can be divided into three types: poorly differentiated, moderately differentiated, and well differentiated. Diagnosis and treatment of different levels of differentiation are crucial to the survival rate and survival time of patients. As the gold standard for liver cancer diagnosis, histopathological images can accurately distinguish liver cancers of different levels of differentiation. Therefore, the study of intelligent classification of histopathological images is of great significance to patients with liver cancer. At present, the classification of histopathological images of liver cancer with different degrees of differentiation has disadvantages such as time-consuming, labor-intensive, and large manual investment. In this context, the importance of intelligent classification of histopathological images is obvious. Methods Based on the development of a complete data acquisition scheme, this paper applies the SENet deep learning model to the intelligent classification of all types of differentiated liver cancer histopathological images for the first time, and compares it with the four deep learning models of VGG16, ResNet50, ResNet_CBAM, and SKNet. The evaluation indexes adopted in this paper include confusion matrix, Precision, recall, F1 Score, etc. These evaluation indexes can be used to evaluate the model in a very comprehensive and accurate way. Results Five different deep learning classification models are applied to collect the data set and evaluate model. The experimental results show that the SENet model has achieved the best classification effect with an accuracy of 95.27%. The model also has good reliability and generalization ability. The experiment proves that the SENet deep learning model has a good application prospect in the intelligent classification of histopathological images. Conclusions This study also proves that deep learning has great application value in solving the time-consuming and laborious problems existing in traditional manual film reading, and it has certain practical significance for the intelligent classification research of other cancer histopathological images. |
| first_indexed | 2024-12-11T15:30:05Z |
| format | Article |
| id | doaj.art-504c6b7bf169427e943c072708b4cf8f |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-12-11T15:30:05Z |
| publishDate | 2022-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-504c6b7bf169427e943c072708b4cf8f2022-12-22T01:00:06ZengBMCBMC Medical Informatics and Decision Making1472-69472022-07-0122111310.1186/s12911-022-01919-1Classification of multi-differentiated liver cancer pathological images based on deep learning attention mechanismChen Chen0Cheng Chen1Mingrui Ma2Xiaojian Ma3Xiaoyi Lv4Xiaogang Dong5Ziwei Yan6Min Zhu7Jiajia Chen8College of Information Science and Engineering, Xinjiang UniversityCollege of Information Science and Engineering, Xinjiang UniversityXinjiang Medical University Cancer HospitalXinjiang Medical University Cancer HospitalCollege of Information Science and Engineering, Xinjiang UniversityXinjiang Medical University Cancer HospitalCollege of Information Science and Engineering, Xinjiang UniversityDepartment of Pathology, Karamay Central Hospital of XinJiang KaramayChangji Vocational and Technical CollegeAbstract Purpose Liver cancer is one of the most common malignant tumors in the world, ranking fifth in malignant tumors. The degree of differentiation can reflect the degree of malignancy. The degree of malignancy of liver cancer can be divided into three types: poorly differentiated, moderately differentiated, and well differentiated. Diagnosis and treatment of different levels of differentiation are crucial to the survival rate and survival time of patients. As the gold standard for liver cancer diagnosis, histopathological images can accurately distinguish liver cancers of different levels of differentiation. Therefore, the study of intelligent classification of histopathological images is of great significance to patients with liver cancer. At present, the classification of histopathological images of liver cancer with different degrees of differentiation has disadvantages such as time-consuming, labor-intensive, and large manual investment. In this context, the importance of intelligent classification of histopathological images is obvious. Methods Based on the development of a complete data acquisition scheme, this paper applies the SENet deep learning model to the intelligent classification of all types of differentiated liver cancer histopathological images for the first time, and compares it with the four deep learning models of VGG16, ResNet50, ResNet_CBAM, and SKNet. The evaluation indexes adopted in this paper include confusion matrix, Precision, recall, F1 Score, etc. These evaluation indexes can be used to evaluate the model in a very comprehensive and accurate way. Results Five different deep learning classification models are applied to collect the data set and evaluate model. The experimental results show that the SENet model has achieved the best classification effect with an accuracy of 95.27%. The model also has good reliability and generalization ability. The experiment proves that the SENet deep learning model has a good application prospect in the intelligent classification of histopathological images. Conclusions This study also proves that deep learning has great application value in solving the time-consuming and laborious problems existing in traditional manual film reading, and it has certain practical significance for the intelligent classification research of other cancer histopathological images.https://doi.org/10.1186/s12911-022-01919-1Histopathological images of liver cancerSENetDegree of differentiation of the whole typeIntelligent classification |
| spellingShingle | Chen Chen Cheng Chen Mingrui Ma Xiaojian Ma Xiaoyi Lv Xiaogang Dong Ziwei Yan Min Zhu Jiajia Chen Classification of multi-differentiated liver cancer pathological images based on deep learning attention mechanism BMC Medical Informatics and Decision Making Histopathological images of liver cancer SENet Degree of differentiation of the whole type Intelligent classification |
| title | Classification of multi-differentiated liver cancer pathological images based on deep learning attention mechanism |
| title_full | Classification of multi-differentiated liver cancer pathological images based on deep learning attention mechanism |
| title_fullStr | Classification of multi-differentiated liver cancer pathological images based on deep learning attention mechanism |
| title_full_unstemmed | Classification of multi-differentiated liver cancer pathological images based on deep learning attention mechanism |
| title_short | Classification of multi-differentiated liver cancer pathological images based on deep learning attention mechanism |
| title_sort | classification of multi differentiated liver cancer pathological images based on deep learning attention mechanism |
| topic | Histopathological images of liver cancer SENet Degree of differentiation of the whole type Intelligent classification |
| url | https://doi.org/10.1186/s12911-022-01919-1 |
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