<?xml version="1.0" encoding="UTF-8"?>
<doi_batch version="4.3.0" xmlns="http://www.crossref.org/doi_resources_schema/4.3.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/doi_resources_schema/4.3.0 http://www.crossref.org/schema/deposit/doi_resources4.3.0.xsd">
<head>
<doi_batch_id>22bfdf33-b5ef-4eda-bd74-23cad4fb121e</doi_batch_id>
<depositor>
<name>beie</name>
<email_address>director@blueeyesintelligence.org</email_address>
</depositor>
</head>
<body>
<doi_citations>
<doi>10.35940/ijitee.L9524.10101221</doi>
<citation_list><citation key="ref0"><doi>10.1111/j.1365-2559.1991.tb00229.x</doi><unstructured_citation>Elston, Christopher W., and Ian O. Ellis. &quot;Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long‐term follow‐up.&quot; Histopathology 19.5, Wiley Online Library (1991): 403-410.</unstructured_citation></citation><citation key="ref1"><doi>10.1016/j.trsl.2017.10.010</doi><unstructured_citation>Robertson, Stephanie, et al. &quot;Digital image analysis in breast pathology-from image processing techniques to artificial intelligence.&quot; Translational Research 194, Elsevier (2018): 19-35.</unstructured_citation></citation><citation key="ref2"><doi>10.1007/978-3-319-93000-8_83</doi><unstructured_citation>Rakhlin, et al. &quot;Deep convolutional neural networks for breast cancer histology image analysis&quot;, International conference image analysis and recognition, pp. 737- 744. Springer, Cham, 2018.</unstructured_citation></citation><citation key="ref3"><doi>10.1109/TBME.2015.2496264</doi><unstructured_citation>Spanhol, et al. &quot;A dataset for breast cancer histopathological image classification.&quot; IEEE Transactions on Biomedical Engineering 63, No. 7 (2015): 1455-1462.</unstructured_citation></citation><citation key="ref4"><doi>10.1007/s11042-020-09518-w</doi><unstructured_citation>Shayma'a, et. al. &quot;Breast cancer masses classification using deep convolutional neural networks and transfer learning.&quot; Multimedia Tools and Applications 79, no. 41 (2020): 30735-30768.</unstructured_citation></citation><citation key="ref5"><doi>10.1007/s10462-020-09825-6</doi><unstructured_citation>Khan et. al. &quot;A survey of the recent architectures of deep convolutional neural networks.&quot; Artificial Intelligence Review 53, no. 8 (2020): 5455-5516.</unstructured_citation></citation><citation key="ref6"><doi>10.1016/j.media.2020.101813</doi><unstructured_citation>Srinidhi et. al. &quot;Deep neural network models for computational histopathology: A survey.&quot; Medical Image Analysis (2020): 101813.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>Robertson, S et. al. &quot;Digital image analysis in breast pathology-From image processing techniques to artificial intelligence&quot;. Transl. Res. 2018, 194, 19-35.</unstructured_citation></citation></citation_list>
</doi_citations>
</body>
</doi_batch>
