<?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>30c9291f-8fa8-4433-8432-4af4a2096676</doi_batch_id>
<depositor>
<name>beie</name>
<email_address>director@blueeyesintelligence.org</email_address>
</depositor>
</head>
<body>
<doi_citations>
<doi>10.35940/ijitee.B9777.13020124</doi>
<citation_list><citation key="ref0"><doi>10.1097/00130747-200108000-00008</doi><unstructured_citation>L. Tabar and P. Dean, Teaching atlas of mammography. New York: Thime,3rd ed., 2001. https://doi.org/10.1097/00130747-200108000-00008</unstructured_citation></citation><citation key="ref1"><unstructured_citation>Valarmathi, Ms P., and V. Radhakrishnan. &quot;Tumor prediction in mammogram using neural network.&quot; Global Journal of Computer Science and Technology (2013).</unstructured_citation></citation><citation key="ref2"><unstructured_citation>R. Schmidt, D.Wolverton, and C. Vyborny, &quot;Computer-aided diagnosis (CAD) in mammography,&quot; in Syllabus: A Categorical Course in Breast Imaging, pp. 199-208, 1995.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>M.L.Giger, &quot;Current issues in mammography,&quot; Proceedings of the 3rd International Workshop on Digital Mammography, pp. 53-59, Chicago, IL, June 1996.</unstructured_citation></citation><citation key="ref4"><doi>10.2214/ajr.162.3.8109525</doi><unstructured_citation>C. Vyborny and M. Giger, &quot;Computer vision and artificial intelligence in mammography,&quot; American Journal of Roentgenology, vol. 162, no. 3, pp. 699-708, 2019. https://doi.org/10.2214/ajr.162.3.8109525</unstructured_citation></citation><citation key="ref5"><doi>10.1148/radiology.177.2.2217807</doi><unstructured_citation>R. Reid, &quot;Professional quality assurance for mammography screening programs,&quot; Radiology, vol. 177, pp. 8-10, 1990. https://doi.org/10.1148/radiology.177.2.2217807</unstructured_citation></citation><citation key="ref6"><doi>10.1177/0272989X9201200110</doi><unstructured_citation>C. Metz and J. Shen, &quot;Gains in accuracy from replicated readings of diagnostic images: Predication and assessment in terms of ROC analysis,&quot; Medical Decision Making, vol. 12, pp. 60-75, 2017. https://doi.org/10.1177/0272989X9201200110</unstructured_citation></citation><citation key="ref7"><unstructured_citation>R. Schmidt, R. Nishikawa, and K. Schreibman, &quot;Computer detection of lesions missed by mammography,&quot; Proceedings of the 2nd International Workshop on Digital Mammography, pp. 289-294, July 10-12 2018.</unstructured_citation></citation><citation key="ref8"><unstructured_citation>R. Schmidt, R. Nishikawa, R. Osnis, K. Schreibman, M. Giger, and K. Doi, &quot;Computerized detection of lesions missed by mammography,&quot; Proceedings of the 3rd International Workshop on Digital Mammography, pp. 105-110, June 9-12 2019.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>&quot;R2 technology pre-market approval (PMA) of the M1000 image checker,&quot; US. Food and Drug Administration (FDA) application #P970058, approved, June 26, 1998.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>R. Highnam and M. Brady, Mammographic Image Analysis. Dordrecht: Kluwer Academic Publishers, 2017.</unstructured_citation></citation><citation key="ref11"><unstructured_citation>L. Bassett, V. Jackson, R. Jahan, Y. Fu, and R. Gold, Diagnosis of diseases of the breast. W.B. Saunders Company, Philadelphia, PA, 1997.</unstructured_citation></citation><citation key="ref12"><doi>10.1097/00004424-198609000-00009</doi><unstructured_citation>C. Metz, &quot;ROC methodology in radiologic imaging,&quot; Investigative Radiology, vol. 21, pp. 720-733, 2018. https://doi.org/10.1097/00004424-198609000-00009</unstructured_citation></citation><citation key="ref13"><unstructured_citation>C. Metz, &quot;Evaluation of digital mammography by ROC analysis,&quot; Proceedings of the 3rd International Workshop on Digital Mammography, pp. 61-68, June 9-12 1996.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>C. Metz, &quot;Receiver operating characteristic (ROC) analysis in medical imaging,&quot; ICRU News, pp. 7-16, 2017.</unstructured_citation></citation><citation key="ref15"><doi>10.1118/1.596358</doi><unstructured_citation>D. Chakraborty, &quot;Maximum likelihood analysis of free-response receiver operating characteristic (FROC) data,&quot; Medical Physics, vol. 16, p. 561, 2017. https://doi.org/10.1118/1.596358</unstructured_citation></citation><citation key="ref16"><doi>10.1148/radiology.174.3.2305073</doi><unstructured_citation>D. Chakraborty and L. Winter, &quot;Free-response methodology: alternate analysis and a new observer-performance experiment,&quot; Radiology, vol. 174, p. 873, 1990. https://doi.org/10.1148/radiology.174.3.2305073</unstructured_citation></citation><citation key="ref17"><doi>10.1118/1.597758</doi><unstructured_citation>R. Swensson, &quot;Unified measurement of observer performance in detecting and localizing target objects on images,&quot; Medical Physics, vol. 23, p. 1709, 2018. https://doi.org/10.1118/1.597758</unstructured_citation></citation><citation key="ref18"><doi>10.1016/S1076-6332(03)80502-0</doi><unstructured_citation>R. Wagner, S. Beiden, and C. Metz, &quot;Continuous vs. categorical data for ROC analysis: Some quantitative considerations,&quot; Academic Radiology, vol. 8, pp. 328-334, 2001. https://doi.org/10.1016/S1076-6332(03)80502-0</unstructured_citation></citation><citation key="ref19"><doi>10.1109/42.875197</doi><unstructured_citation>N. Karssemeijer and W. Veldkamp, &quot;Normalisation of local contrast in mammograms,&quot; IEEE Transactions on Medical Imaging, vol. 19, no. 7, pp. 731-738, 2017. https://doi.org/10.1109/42.875197</unstructured_citation></citation><citation key="ref20"><doi>10.1117/12.467084</doi><unstructured_citation>K. McLoughlin, P. Bones, and P. Dachman, &quot;Connective tissue representation for detection of microcalcification in digital mammograms,&quot; Proceedings of SPIE Medical Image 2002: Image Proceesing, vol. 4684, pp. 1246-1256, 2017. https://doi.org/10.1117/12.467084</unstructured_citation></citation><citation key="ref21"><doi>10.35940/ijeat.E9805.069520</doi><unstructured_citation>Kinani, L., &amp; Alqasemi, U. (2020). Computer Aided Diagnosis of Mammography Cancer. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 5, pp. 725-731). https://doi.org/10.35940/ijeat.e9805.069520</unstructured_citation></citation><citation key="ref22"><doi>10.35940/ijrte.D8094.118419</doi><unstructured_citation>Jani, K. K., Srivastava, S., &amp; Srivastava, R. (2019). Computer-Aided Diagnosis for Capsule Endoscopy: From Inception to Future. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 4, pp. 12261-12273). https://doi.org/10.35940/ijrte.d8094.118419</unstructured_citation></citation><citation key="ref23"><doi>10.35940/ijitee.H9164.0711822</doi><unstructured_citation>Voona, V. N., Sathwik, E., Jayanth, T. S., &amp; Rohan, T. (2022). Brain Segmentation using MATLAB. In International Journal of Innovative Technology and Exploring Engineering (Vol. 11, Issue 8, pp. 43-49). https://doi.org/10.35940/ijitee.h9164.0711822</unstructured_citation></citation><citation key="ref24"><doi>10.35940/ijmh.I0877.054920</doi><unstructured_citation>Nasir, F. M., &amp; Watabe, H. (2020). Validation of the Image Registration Technique from Functional Near Infrared Spectroscopy (fNIRS) Signal and Positron Emission Tomography (PET) Image. In International Journal of Management and Humanities (Vol. 4, Issue 9, pp. 63-69). https://doi.org/10.35940/ijmh.i0877.054920</unstructured_citation></citation><citation key="ref25"><doi>10.54105/ijamst.C3016.081421</doi><unstructured_citation>Rehman, F., Ali, S. S., Panhwar, H., Phul, Dr. A. H., Rajpar, S. A., Ahmed, S., Rabbani, S., &amp; Mehmood, T. (2021). Brain Tumor Detection from MR Images using Image Process Techniques and Tools in Matlab Software. In International Journal of Advanced Medical Sciences and Technology (Vol. 1, Issue 4, pp. 1-4). https://doi.org/10.54105/ijamst.c3016.081421</unstructured_citation></citation></citation_list>
</doi_citations>
</body>
</doi_batch>
