{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T11:43:02Z","timestamp":1792410182534,"version":"4.4.0"},"reference-count":24,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2020,7,18]],"date-time":"2020-07-18T00:00:00Z","timestamp":1595030400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2020,9]]},"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>Non\u2010Hodgkin's lymphoma (NHL) is a serious malignant disease. Delayed diagnosis will cause anemia, increased intracranial pressure, organ failure, and even lead to death. The current main trend in this area is to use deep learning (DL) for disease diagnosis. Extracting classification information from the digital pathology images by DL may realize the automated qualitative and quantitative analysis of NHL. Previously, DL has been used to classify NHL digital pathology images with some success. However, shortcomings still exist in the data preprocessing methods and feature extraction. Therefore, this paper presents a method for the classification of NHL subtypes based on the fusion of transfer learning (TL) and principal component analysis (PCA).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>First, the NHL digital pathology images were preprocessed by image division and segmentation and then input into the transfer models for fine\u2010tuning and feature extraction. Second, PCA was used to map the extracted features. Finally, a neural network was used as a classifier to classify the mapped features. During the fine\u2010tuning of the transfer models, two methods, freezing all feature extraction layers and fine\u2010tuning all layers, were employed to select the optimal model with the best classification result among all the preselected transfer models. On this basis, the use of freezing the layers' location was discussed and analyzed.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The results show that the proposed method achieved average fivefold cross\u2010validation accuracies of 100%, 99.73%, and 99.20% for chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and mantle cell lymphoma (MCL) tumor, and each category has standard deviations 0.00, 0.53, and 0.65, respectively, in the NHL reference dataset. The overall classification accuracy for fivefold cross\u2010validation is 98.93%, which is an increase of 1.26% compared to the latest reported methods, having a lower standard deviation (1.00).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The method proposed in this paper achieves a high classification accuracy and strong model generalization for the classification of NHL, which makes it possible to conduct intelligent classification of NHL in clinical practice. Our proposed method has definite clinical value and research significance.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.14357","type":"journal-article","created":{"date-parts":[[2020,6,27]],"date-time":"2020-06-27T21:55:40Z","timestamp":1593294940000},"page":"4241-4253","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Classification of digital pathological images of non\u2010Hodgkin's lymphoma subtypes based on the fusion of transfer learning and principal component analysis"],"prefix":"10.1002","volume":"47","author":[{"given":"Jianfei","family":"Zhang","sequence":"first","affiliation":[{"name":"School of computer and control engineering Qiqihar university Qiqihar 161006 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wensheng","family":"Cui","sequence":"additional","affiliation":[{"name":"School of computer and control engineering Qiqihar university Qiqihar 161006 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyan","family":"Guo","sequence":"additional","affiliation":[{"name":"School of computer and control engineering Qiqihar university Qiqihar 161006 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Wang","sequence":"additional","affiliation":[{"name":"School of computer and control engineering Qiqihar university Qiqihar 161006 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Wang","sequence":"additional","affiliation":[{"name":"School of computer and control engineering Qiqihar university Qiqihar 161006 China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2020,7,18]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0929-6646(11)60013-2"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2015.61.9767"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1126\/scitranslmed.3005899"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.07.007"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-2141.2011.08772.x"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1155\/2012\/904367"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2016.17216"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature21056"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2908724"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.4103\/2153-3539.186902"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-018-0265-3"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1177\/1533033819858363"},{"key":"e_1_2_8_15_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JPHOT.2019.2934484","article-title":"On OCT image classification via deep learning","volume":"11","author":"Wang DP","year":"2019","journal-title":"IEEE Photon J"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00417-018-04224-8"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-019-0349-x"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2925059"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.06.087"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2528162"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.3.3.034501"},{"key":"e_1_2_8_22_1","unstructured":"SimonyanK ZissermanA.Very Deep Convolutional Networks for Large\u2010Scale Image Recognition. arXiv Preprint: arXiv1409.1556;2014."},{"key":"e_1_2_8_23_1","doi-asserted-by":"crossref","unstructured":"HeK ZhangX RenS JianS.Deep residual learning for image Recognition. 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