{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T20:44:50Z","timestamp":1788813890538,"version":"build-2803163510"},"reference-count":24,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T00:00:00Z","timestamp":1616630400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["THC"],"published-print":{"date-parts":[[2021,3,25]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3233\/thc-218031","type":"journal-article","created":{"date-parts":[[2021,3,2]],"date-time":"2021-03-02T13:12:55Z","timestamp":1614690775000},"page":"335-344","source":"Crossref","is-referenced-by-count":30,"title":["Research on the classification of lymphoma pathological images based on deep residual neural network"],"prefix":"10.1177","volume":"29","author":[{"given":"Xiaoli","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250355, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kuixing","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250355, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mei","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250355, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Yang","sequence":"additional","affiliation":[{"name":"Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China"},{"name":"Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"issue":"5","key":"10.3233\/THC-218031_ref1","first-page":"756","article-title":"Analysis of survival and prognostic factors in patients with malignant lymphoma after autologous hematopoietic stem cell 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