ArticleTechnology and health care : official journal of the European Society for Engineering and Medicine2021
Research on the classification of lymphoma pathological images based on deep residual neural network.
Article in Technology and health care : official journal of the European Society for Engineering and Medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence in Lymphoma Histopathology: Systematic Review.Journal of medical Internet research · 2025Pooled it
- A multimodal explainable ai framework to assist in the differential diagnosis of head and neck reactive follicular hyperplasia and follicular lymphoma: an international multicentre study.Virchows Archiv : an international journal of pathology · 2026Article
- Artificial Intelligence and Data Science Methods for Automatic Detection of White Blood Cells in Images.Journal of imaging informatics in medicine · 2026Review
- A novel hybrid convolutional and transformer network for lymphoma classification.Scientific reports · 2025Article
- Role of artificial intelligence in haematolymphoid diagnostics.Histopathology · 2025Review
- PrecisionLymphoNet: Advancing Malignant Lymphoma Diagnosis via Ensemble Transfer Learning with CNNs.Diagnostics (Basel, Switzerland) · 2024Article
- Hybrid Models Based on Fusion Features of a CNN and Handcrafted Features for Accurate Histopathological Image Analysis for Diagnosing Malignant Lymphomas.Diagnostics (Basel, Switzerland) · 2023Article
- Article
- Application of machine learning in the management of lymphoma: Current practice and future prospects.Digital healthReview
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundMalignant 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.
objectiveAt present, an automatic classification technology is urgently needed to assist doctors in analyzing the type of lymphoma.
methodsIn 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.
resultsThe 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.
conclusionsThe network model can provide an objective basis for doctors to diagnose lymphoma types.
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