REFERENCES
1. Sizilio GR, Leite CR, Guerreiro AM, Neto AD. Fuzzy method for pre-diagnosis of breast cancer from the Fine Needle Aspirate analysis. Biomed Eng Online. 2012;11:83.
2. Ginsburg O, Bray F, Coleman MP, et al. The global burden of women's cancers: a grand challenge in global health. Lancet. 2017;389:847-60.
3. Badawy E, Elnaggar R, Soliman SAM, Elmesidy DS. Performance of AI-aided mammography in breast cancer diagnosis: does breast density matter? Egypt J Radiol Nucl Med. 2023;54:1129.
4. Díaz O, Rodríguez-Ruíz A, Sechopoulos I. Artificial intelligence for breast cancer detection: technology, challenges, and prospects. Eur J Radiol. 2024;175:111457.
5. DeSantis CE, Ma J, Gaudet MM, et al. Breast cancer statistics, 2019. CA Cancer J Clin. 2019;69:438-51.
6. Ahmad J, Akram S, Jaffar A, et al. Deep learning empowered breast cancer diagnosis: advancements in detection and classification. PLoS One. 2024;19:e0304757.
7. Al-Karawi D, Al-Zaidi S, Helael KA, et al. A review of artificial intelligence in breast imaging. Tomography. 2024;10:705-26.
8. Smith HO, Kammerer-Doak DN, Barbo DM, Sarto GE. Hormone replacement therapy in the menopause: a pro opinion. CA Cancer J Clin. 1996;46:343-63.
9. Alkayyali ZK, Taha AM, Zarandah QM, Abunasser BS, Barhoom AM, Abu-Naser SS. Advancements in AI for medical imaging: transforming diagnosis and treatment. Int J Acad Eng Res. 2024;8:8-15. Available from: https://philarchive.org/rec/ALKAIA [Last accessed on 26 Nov 2025].
10. Rojas K, Stuckey A. Breast cancer epidemiology and risk factors. Clin Obstet Gynecol. 2016;59:651-72.
11. Haber D. Prophylactic oophorectomy to reduce the risk of ovarian and breast cancer in carriers of BRCA mutations. N Engl J Med. 2002;346:1660-2.
12. Malone KE, Daling JR, Thompson JD, O'Brien CA, Francisco LV, Ostrander EA. BRCA1 mutations and breast cancer in the general population: analyses in women before age 35 years and in women before age 45 years with first-degree family history. JAMA. 1998;279:922-9.
13. Feng Y, Spezia M, Huang S, et al. Breast cancer development and progression: risk factors, cancer stem cells, signaling pathways, genomics, and molecular pathogenesis. Genes Dis. 2018;5:77-106.
14. Ni H, Kumbrink J, Mayr D, et al. Molecular prognostic factors for distant metastases in premenopausal patients with HR+/HER2- early breast cancer. J Pers Med. 2021;11:835.
15. Wang S, Guo X, Ma J, et al. AWCDL: automatic weight calibration deep learning for detecting HER2 status in whole-slide breast cancer image. Intell Oncol. 2025;1:128-38.
16. Migowski A. [Early detection of breast cancer and the interpretation of results of survival studies]. Cien Saude Colet. 2015;20:1309.
17. Dai X, Li T, Bai Z, et al. Breast cancer intrinsic subtype classification, clinical use and future trends. Am J Cancer Res. 2015;5:2929-43.
18. Bodily WR, Shirts BH, Walsh T, et al. Effects of germline and somatic events in candidate BRCA-like genes on breast-tumor signatures. PLoS One. 2020;15:e0239197.
19. Batool Z, Kamal MA, Shen B. Advancements in triple-negative breast cancer sub-typing, diagnosis and treatment with assistance of artificial intelligence : a focused review. J Cancer Res Clin Oncol. 2024;150:383.
20. Carriero A, Groenhoff L, Vologina E, Basile P, Albera M. Deep learning in breast cancer imaging: state of the art and recent advancements in early 2024. Diagnostics. 2024;14:848.
21. Chauhan AS, Singh R, Priyadarshi N, Twala B, Suthar S, Swami S. Unleashing the power of advanced technologies for revolutionary medical imaging: pioneering the healthcare frontier with artificial intelligence. Discov Artif Intell. 2024;4:58.
24. Makki J. Diversity of breast carcinoma: histological subtypes and clinical relevance. Clin Med Insights Pathol. 2015;8:23-31.
25. Chang J, Chaudhuri O. Beyond proteases: basement membrane mechanics and cancer invasion. J Cell Biol. 2019;218:2456-69.
26. Onega T, Goldman LE, Walker RL, et al. Facility mammography volume in relation to breast cancer screening outcomes. J Med Screen. 2016;23:31-7.
27. Testa U, Castelli G, Pelosi E. Breast cancer: a molecularly heterogenous disease needing subtype-specific treatments. Med Sci. 2020;8:18.
28. Mukherjee S, Sengupta A, Preetam S, Das T, Bhattacharya T, Thorat N. Effects of fatty acid esters on mechanical, thermal, microbial, and moisture barrier properties of carboxymethyl cellulose-based edible films. Carbohydr Polym Technol Appl. 2024;7:100505.
29. Preetam S, Mondal S, Priya S, et al. Targeting tumour markers in ovarian cancer treatment. Clin Chim Acta. 2024;559:119687.
30. Jørgensen KJ, Gyøtzsche PC. Unclear methods in estimate of screening effect in women ages 40 to 49 years. Cancer. 2012;118:1170.
31. Elahi R, Nazari M. An updated overview of radiomics-based artificial intelligence (AI) methods in breast cancer screening and diagnosis. Radiol Phys Technol. 2024;17:795-818.
32. Ijaz MF, Woźniak M. Editorial: recent advances in deep learning and medical imaging for cancer treatment. Cancers. 2024;16:700.
34. Roganovic D, Djilas D, Vujnovic S, Pavic D, Stojanov D. Breast MRI, digital mammography and breast tomosynthesis: comparison of three methods for early detection of breast cancer. Bosn J Basic Med Sci. 2015;15:64-8.
36. Lewis TC, Pizzitola VJ, Giurescu ME, et al. Contrast-enhanced digital mammography: a single-institution experience of the first 208 cases. Breast J. 2017;23:67-76.
37. Xu P, Peng Y, Sun M, Yang X. SU-E-I-81: targeting of HER2-expressing tumors with dual PET-MR imaging probes. Med Phys. 2015;42:3260-3260.
38. Chen T, Artis F, Dubuc D, Fournie JJ, Poupot M, Grenier K. Microwave biosensor dedicated to the dielectric spectroscopy of a single alive biological cell in its culture medium. In 2013 IEEE MTT-S International Microwave Symposium Digest (MTT), Seattle, WA, USA; 2-7 June 2013.
39. Hassan AM, El-Shenawee M. Review of electromagnetic techniques for breast cancer detection. IEEE Rev Biomed Eng. 2011;4:103-18.
40. Rentiya ZS, Mandal S, Inban P, et al. Revolutionizing breast cancer detection with artificial intelligence (AI) in radiology and radiation oncology: a systematic review. Cureus. 2024;16:e57619.
41. Uchikov P, Khalid U, Dedaj-Salad GH, et al. Artificial intelligence in breast cancer diagnosis and treatment: advances in imaging, pathology, and personalized care. Life. 2024;14:1451.
42. Andropova US, Tebeneva NA, Tarasenkov AN, et al. The effect of hafnium alkoxysiloxane precursor structure of disperse phase on the morphology of nanocomposites based on polyaryleneetherketone. Polym Sci Ser B. 2017;59:202-9.
43. Lu G, Li S, Guo Z, et al. Imparting functionality to a metal-organic framework material by controlled nanoparticle encapsulation. Nat Chem. 2012;4:310-6.
44. Makki B, Chitti K, Behravan A, Alouini M. A survey of NOMA: current status and open research challenges. IEEE Open J Commun Soc. 2020;1:179-89.
45. Tsai CH, Fordyce RE. Juvenile morphology in baleen whale phylogeny. Naturwissenschaften. 2014;101:765-9.
46. Veta M, Heng YJ, Stathonikos N, et al. Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge. Med Image Anal. 2019;54:111-21.
47. Vanni S, Caputo TM, Cusano AM, et al. Engineered anti-HER2 drug delivery nanosystems for the treatment of breast cancer. Nanoscale. 2025;17:9436-57.
48. Segovia-Mendoza M, Morales-Montor J. Immune tumor microenvironment in breast cancer and the participation of estrogen and its receptors in cancer physiopathology. Front Immunol. 2019;10:348.
49. Mansouri K, Karmaus AL, Fitzpatrick J, et al. CATMoS: collaborative acute toxicity modeling suite. Environ Health Perspect. 2021;129:47013.
50. Aponte-López A, Fuentes-Pananá EM, Cortes-Muñoz D, Muñoz-Cruz S. Mast cell, the neglected member of the tumor microenvironment: role in breast cancer. J Immunol Res. 2018;2018:2584243.
51. Mao L, Jin H, Wang M, et al. Neurologic manifestations of hospitalized patients with coronavirus disease 2019 in Wuhan, China. JAMA Neurol. 2020;77:683-90.
52. Bédard A, Basagaña X, Anto JM, et al. Mobile technology offers novel insights into the control and treatment of allergic rhinitis: the MASK study. J Allergy Clin Immunol. 2019;144:135-43.e6.
53. Osareh A, Shadgar B. Machine learning techniques to diagnose breast cancer. In 2010 5th international symposium on health informatics and bioinformatics. Ankara, Turkey; 20-22 April 2010.
54. Darbandi MR, Darbandi M, Darbandi S, Bado I, Hadizadeh M, Khorram Khorshid HR. Artificial intelligence breakthroughs in pioneering early diagnosis and precision treatment of breast cancer: a multimethod study. Eur J Cancer. 2024;209:114227.
55. Diaby V, Adunlin G, Ali AA, et al. Cost-effectiveness analysis of 1st through 3rd line sequential targeted therapy in HER2-positive metastatic breast cancer in the United States. Breast Cancer Res Treat. 2016;160:187-96.
56. Klimov S, Miligy IM, Gertych A, et al. A whole slide image-based machine learning approach to predict ductal carcinoma in situ (DCIS) recurrence risk. Breast Cancer Res. 2019;21:83.
57. Aringer M, Costenbader K, Daikh D, et al. 2019 European League against Rheumatism/American College of Rheumatology classification criteria for systemic lupus erythematosus. Ann Rheum Dis. 2019;78:1151-9.
58. Attal M, Richardson PG, Rajkumar SV, et al. Isatuximab plus pomalidomide and low-dose dexamethasone versus pomalidomide and low-dose dexamethasone in patients with relapsed and refractory multiple myeloma (ICARIA-MM): a randomised, multicentre, open-label, phase 3 study. Lancet. 2019;394:2096-107.
59. Kim C, Gao R, Sei E, et al. Chemoresistance evolution in triple-negative breast cancer delineated by single-cell sequencing. Cell. 2018;173:879-93.e13.
60. Alharbi J, Jackson D, Usher K. The potential for COVID-19 to contribute to compassion fatigue in critical care nurses. J Clin Nurs. 2020;29:2762-4.
61. Seroussi H, Nakayama Y, Larour E, et al. Continued retreat of Thwaites Glacier, West Antarctica, controlled by bed topography and ocean circulation. Geophysical Research Letters. 2017;44:6191-9.
62. Sandbank J, Bataillon G, Nudelman A, et al. Validation and real-world clinical application of an artificial intelligence algorithm for breast cancer detection in biopsies. NPJ Breast Cancer. 2022;8:129.
63. Kocher MR, Chamberlin J, Waltz J, et al. Tumor burden of lung metastases at initial staging in breast cancer patients detected by artificial intelligence as a prognostic tool for precision medicine. Heliyon. 2022;8:e08962.
64. Garrucho L, Kushibar K, Osuala R, et al. High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection. Front Oncol. 2022;12:1044496.
65. Sirjani N, Ghelich Oghli M, Kazem Tarzamni M, et al. A novel deep learning model for breast lesion classification using ultrasound images: a multicenter data evaluation. Phys Med. 2023;107:102560.
66. Meng M, Zhang M, Shen D, He G. Differentiation of breast lesions on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using deep transfer learning based on DenseNet201. Medicine. 2022;101:e31214.
67. Tomczak K, Czerwińska P, Wiznerowicz M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol. 2015;19:A68-77.
68. Xu J, Wu P, Chen Y, Meng Q, Dawood H, Dawood H. A hierarchical integration deep flexible neural forest framework for cancer subtype classification by integrating multi-omics data. BMC Bioinf. 2019;20:527.
69. Simidjievski N, Bodnar C, Tariq I, et al. Variational autoencoders for cancer data integration: design principles and computational practice. Front Genet. 2019;10:1205.
70. Mariette J, Villa-Vialaneix N. Unsupervised multiple kernel learning for heterogeneous data integration. Bioinformatics. 2018;34:1009-15.
71. Huang K, Xiao C, Glass LM, Critchlow CW, Gibson G, Sun J. Machine learning applications for therapeutic tasks with genomics data. Patterns. 2021;2:100328.
72. Ali M, Aittokallio T. Machine learning and feature selection for drug response prediction in precision oncology applications. Biophys Rev. 2019;11:31-9.
73. Gautam P, Jaiswal A, Aittokallio T, Al-Ali H, Wennerberg K. Phenotypic screening combined with machine learning for efficient identification of breast cancer-selective therapeutic targets. Cell Chem Biol. 2019;26:970-9.e4.
74. Li M, Ni P, Chen X, Wang J, Wu FX, Pan Y. Construction of refined protein interaction network for predicting essential proteins. IEEE/ACM Trans Comput Biol Bioinform. 2019;16:1386-97.
75. Choudhery S, Gomez-Cardona D, Favazza CP, et al. MRI radiomics for assessment of molecular subtype, pathological complete response, and residual cancer burden in breast cancer patients treated with neoadjuvant chemotherapy. Acad Radiol. 2022;29 Suppl 1:S145-54.
76. Dercle L, Ammari S, Bateson M, et al. Limits of radiomic-based entropy as a surrogate of tumor heterogeneity: ROI-area, acquisition protocol and tissue site exert substantial influence. Sci Rep. 2017;7:7952.
77. Prud'homme C, Deschamps F, Allorant A, et al. Image-guided tumour biopsies in a prospective molecular triage study (MOSCATO-01): what are the real risks? Eur J Cancer. 2018;103:108-19.
78. Wilkinson L, Thomas V, Sharma N. Microcalcification on mammography: approaches to interpretation and biopsy. Br J Radiol. 2017;90:20160594.
79. Qi X, Zhang L, Chen Y, et al. Automated diagnosis of breast ultrasonography images using deep neural networks. Med Image Anal. 2019;52:185-98.
80. Fischer U, Baum F, Obenauer S, et al. Comparative study in patients with microcalcifications: full-field digital mammography vs screen-film mammography. Eur Radiol. 2002;12:2679-83.
81. Lima R, Del Fiol FS, Balcão VM. Prospects for the use of new technologies to combat multidrug-resistant bacteria. Front Pharmacol. 2019;10:692.
82. Fischer U, Kopka L, Grabbe E. Breast carcinoma: effect of preoperative contrast-enhanced MR imaging on the therapeutic approach. Radiology. 1999;213:881-8.
83. Liu Z, Wang S, Dong D, et al. The applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges. Theranostics. 2019;9:1303-22.
84. Bobola MS, Chen L, Ezeokeke CK, et al. A review of recent advances in ultrasound, placed in the context of pain diagnosis and treatment. Curr Pain Headache Rep. 2018;22:60.
85. Kontos D, Bakic PR, Troxel AB, Conant EF, Maidment ADA. Digital breast tomosynthesis parenchymal texture analysis for breast cancer risk estimation: a preliminary study. In: Krupinski EA, editor. Digital mammography. Berlin: Springer Berlin Heidelberg; 2008. pp. 681-8.
86. Pesapane F, Rotili A, Agazzi GM, et al. Recent radiomics advancements in breast cancer: lessons and pitfalls for the next future. Curr Oncol. 2021;28:2351-72.
87. Limkin EJ, Reuzé S, Carré A, et al. The complexity of tumor shape, spiculatedness, correlates with tumor radiomic shape features. Sci Rep. 2019;9:4329.
88. Mariscotti G, Houssami N, Durando M, et al. Accuracy of mammography, digital breast tomosynthesis, ultrasound and MR imaging in preoperative assessment of breast cancer. Anticancer Res. 2014;34:1219-25.
89. Niu S, Wang X, Zhao N, et al. Radiomic evaluations of the diagnostic performance of DM, DBT, DCE MRI, DWI, and their combination for the diagnosisof breast cancer. Front Oncol. 2021;11:725922.
90. Chen JH, Gulsen G, Su MY. Imaging breast density: established and emerging modalities. Transl Oncol. 2015;8:435-45.
91. Smith BE, Selfe J, Thacker D, et al. Incidence and prevalence of patellofemoral pain: a systematic review and meta-analysis. PLoS One. 2018;13:e0190892.
92. Niklason LT, Kopans DB, Hamberg LM. Digital breast imaging: tomosynthesis and digital subtraction mammography. Breast Dis. 1998;10:151-64.
93. Mao N, Yin P, Li Q, et al. Radiomics nomogram of contrast-enhanced spectral mammography for prediction of axillary lymph node metastasis in breast cancer: a multicenter study. Eur Radiol. 2020;30:6732-9.
94. Stelzer PD, Steding O, Raudner MW, Euller G, Clauser P, Baltzer PAT. Combined texture analysis and machine learning in suspicious calcifications detected by mammography: Potential to avoid unnecessary stereotactical biopsies. Eur J Radiol. 2020;132:109309.
95. Zhou J, Tan H, Bai Y, et al. Evaluating the HER-2 status of breast cancer using mammography radiomics features. Eur J Radiol. 2019;121:108718.
96. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18:500-10.
97. Tran WT, Sadeghi-Naini A, Lu FI, et al. Computational radiology in breast cancer screening and diagnosis using artificial intelligence. Can Assoc Radiol J. 2021;72:98-108.
98. Bahl M. Artificial intelligence: a primer for breast imaging radiologists. J Breast Imaging. 2020;2:304-14.
100. Dheeba J, Albert Singh N, Tamil Selvi S. Computer-aided detection of breast cancer on mammograms: a swarm intelligence optimized wavelet neural network approach. J Biomed Inform. 2014;49:45-52.
101. Yala A, Schuster T, Miles R, Barzilay R, Lehman C. A deep learning model to triage screening mammograms: a simulation study. Radiology. 2019;293:38-46.
102. Fujioka T, Kubota K, Mori M, et al. Distinction between benign and malignant breast masses at breast ultrasound using deep learning method with convolutional neural network. Jpn J Radiol. 2019;37:466-72.
103. Raya-Povedano JL, Romero-Martín S, Elías-Cabot E, Gubern-Mérida A, Rodríguez-Ruiz A, Álvarez-Benito M. AI-based strategies to reduce workload in breast cancer screening with mammography and tomosynthesis: a retrospective evaluation. Radiology. 2021;300:57-65.
104. Fotin SV, Yin Y, Haldankar H, Hoffmeister JW, Periaswamy S. Detection of soft tissue densities from digital breast tomosynthesis: comparison of conventional and deep learning approaches. In Medical Imaging; 2016.
105. Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. EEE Trans Pattern Anal Mach Intell. 2017;39:640-51.
106. Lee J, Nishikawa RM. Automated mammographic breast density estimation using a fully convolutional network. Med Phys. 2018;45:1178-90.
107. Hai J, Qiao K, Chen J, et al. Fully convolutional DenseNet with multiscale context for automated breast tumor segmentation. J Healthc Eng. 2019;2019:8415485.
108. Xu S, Adeli E, Cheng J, et al. Mammographic mass segmentation using multichannel and multiscale fully convolutional networks. Int J Imaging Syst Tech. 2020;30:1095-107.
109. Sathyan A, Martis D, Cohen K. Mass and calcification detection from digital mammograms using unets. In 2020 7th International Conference on Soft Computing & Machine Intelligence (ISCMI). Stockholm, Sweden; 14-15 November 2020.
110. Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF, editors. Medical image computing and computer-assisted intervention - MICCAI 2015. Cham: Springer International Publishing; 2015. pp. 234-41.
111. AlGhamdi M, Abdel-Mottaleb M, Collado-Mesa F. DU-net: convolutional network for the detection of arterial calcifications in mammograms. IEEE Trans Med Imaging. 2020;39:3240-9.
113. Ratner AJ, Ehrenberg H, Hussain Z, Dunnmon J, Ré C. Learning to compose domain-specific transformations for data augmentation. In Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017, pp. 3239-49.
114. Hussain Z, Gimenez F, Yi D, Rubin D. Differential data augmentation techniques for medical imaging classification tasks. AMIA Annu Symp Proc. 2018;2017:979-84.
115. Warnes CA, Williams RG, Bashore TM, et al. ACC/AHA 2008 guidelines for the management of adults with congenital heart disease: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (writing committee to develop guidelines on the management of adults with congenital heart disease). Circulation. 2008;118:e714-833.
116. Yamashita R, Nishio M, Do RKG, Togashi K. Convolutional neural networks: an overview and application in radiology. Insights Imaging. 2018;9:611-29.
117. Wu E, Wu K, Cox D, Lotter W. Conditional infilling GANs for data augmentation in mammogram classification. In: Image analysis for moving organ, breast, and thoracic images. 2018, pp. 98-106.
118. Wu E, Wu K, Lotter W. Synthesizing lesions using contextual GANs improves breast cancer classification on mammograms. arXiv. 2020:2006.00086.
119. Korkinof D, Rijken T, O'Neill M, Yearsley J, Harvey H, Glocker B. High-resolution mammogram synthesis using progressive generative adversarial networks. arXiv. 2018:1807.03401.
120. Becker AS, Jendele L, Skopek O, et al. Injecting and removing malignant features in mammography with CycleGAN: investigation of an automated adversarial attack using neural networks. arXiv. 2018:1811.07767.
121. Parisi GI, Kemker R, Part JL, Kanan C, Wermter S. Continual lifelong learning with neural networks: a review. Neural Netw. 2019;113:54-71.
122. Mehdy MM, Ng PY, Shair EF, Saleh NIM, Gomes C. Artificial neural networks in image processing for early detection of breast cancer. Comput Math Methods Med. 2017;2017:2610628.
123. Wu Y, Giger ML, Doi K, Vyborny CJ, Schmidt RA, Metz CE. Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer. Radiology. 1993;187:81-7.
124. Fogel DB, Wasson EC 3rd, Boughton EM, Porto VW. Evolving artificial neural networks for screening features from mammograms. Artif Intell Med. 1998;14:317-26.
125. Quintanilla-Domínguez J, Cortina-Januchs M, Jevtić A, Andina D, Barrón-Adame J, Vega-Corona A. Combination of nonlinear filters and ANN for detection of microcalcifications in digitized mammography. In 2009 IEEE International Conference on Systems, Man and Cybernetics. San Antonio, TX, USA; 11-14 October 2009.
126. Papadopoulos A, Fotiadis DI, Likas A. Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines. Artif Intell Med. 2005;34:141-50.
127. García-Manso A, García-Orellana CJ, González-Velasco H, Gallardo-Caballero R, Macías MM. Consistent performance measurement of a system to detect masses in mammograms based on blind feature extraction. Biomed Eng Online. 2013;12:2.
128. Hupse R, Samulski M, Lobbes M, et al. Standalone computer-aided detection compared to radiologists' performance for the detection of mammographic masses. Eur Radiol. 2013;23:93-100.
129. Tan M, Qian W, Pu J, Liu H, Zheng B. A new approach to develop computer-aided detection schemes of digital mammograms. Phys Med Biol. 2015;60:4413-27.
130. Mahersia H, Boulehmi H, Hamrouni K. Development of intelligent systems based on Bayesian regularization network and neuro-fuzzy models for mass detection in mammograms: a comparative analysis. Comput Methods Programs Biomed. 2016;126:46-62.
131. Mohammed MA, Al-Khateeb B, Rashid AN, Ibrahim DA, Abd Ghani MK, Mostafa SA. Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images. Comput Electr Eng. 2018;70:871-82.
132. Bi WL, Hosny A, Schabath MB, et al. Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin. 2019;69:127-57.
133. Kaushal C, Bhat S, Koundal D, Singla A. Recent trends in computer assisted diagnosis (CAD) system for breast cancer diagnosis using histopathological images. IRBM. 2019;40:211-27.
134. Ramadan SZ. Methods used in computer-aided diagnosis for breast cancer detection using mammograms: a review. J Healthc Eng. 2020;2020:9162464.
135. Reinhard E, Adhikhmin M, Gooch B, Shirley P. Color transfer between images. IEEE Comput Grap Appl. 2001;21:34-41.
136. Winsberg F, Elkin M, Macy J, Bordaz V, Weymouth W. Detection of radiographic abnormalities in mammograms by means of optical scanning and computer analysis. Radiology. 1967;89:211-5.
137. Jiang Y, Nishikawa RM, Wolverton DE, et al. Malignant and benign clustered microcalcifications: automated feature analysis and classification. Radiology. 1996;198:671-8.
138. Gao Y, Geras KJ, Lewin AA, Moy L. New frontiers: an update on computer-aided diagnosis for breast imaging in the age of artificial intelligence. Am J Roentgenol. 2019;212:300-7.
139. Freer TW, Ulissey MJ. Screening mammography with computer-aided detection: prospective study of 12,860 patients in a community breast center. Radiology. 2001;220:781-6.
140. de Figueiredo G, Ingrisch M, Fallenberg EM. Digital analysis in breast imaging. Breast Care. 2019;14:142-50.
141. Birkenfeld JS, Tucker-Schwartz JM, Soenksen LR, Avilés-Izquierdo JA, Marti-Fuster B. Computer-aided classification of suspicious pigmented lesions using wide-field images. Comput Methods Programs Biomed. 2020;195:105631.
142. Giger ML, Chan HP, Boone J. Anniversary paper: history and status of CAD and quantitative image analysis: the role of medical physics and AAPM. Med Phys. 2008;35:5799-820.
143. Ghammraoui B, Makeev A, Zidan A, Alayoubi A, Glick SJ. Classification of breast microcalcifications using dual-energy mammography. J Med Imaging. 2019;6:013502.
144. Nemoto M, Masutani Y, Nomura Y, et al. [Machine learning for computer-aided diagnosis]. Igaku Butsuri. 2016;36:29-34.
145. Sampat MP, Markey MK, Bovik AC. 10.4 - Computer-aided detection and diagnosis in mammography. In Handbook of image and video processing; 2005, pp. 1195-217.
146. Oza P, Sharma P, Patel S, Bruno A. A bottom-up review of image analysis methods for suspicious region detection in mammograms. J Imaging. 2021;7:190.
147. Raguso G, Ancona A, Chieppa L, et al. Application of fractal analysis to mammography. Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:3182-5.
148. Eltonsy NH, Tourassi GD, Elmaghraby AS. A concentric morphology model for the detection of masses in mammography. IEEE Trans Med Imaging. 2007;26:880-9.
149. Rangayyan RM, Mudigonda NR, Desautels JE. Boundary modelling and shape analysis methods for classification of mammographic masses. Med Biol Eng Comput. 2000;38:487-96.
150. Surendiran B, Vadivel A. Mammogram mass classification using various geometric shape and margin features for early detection of breast cancer. Int J Med Eng Inf. 2012;4:36.
151. Mustra M, Bozek J, Grgic M. Nipple detection in craniocaudal digital mammograms. In 2009 International Symposium ELMAR. Zadar, Croatia; 28-30 September 2009. Available from: https://ieeexplore.ieee.org/document/5342871 [Last accessed on 26 Nov 2025].
152. Li H, Meng X, Wang T, Tang Y, Yin Y. Breast masses in mammography classification with local contour features. Biomed Eng Online. 2017;16:44.
153. Elmoufidi A, El Fahssi K, Jai-Andaloussi S, Sekkaki A, Gwenole Q, Lamard M. Anomaly classification in digital mammography based on multiple-instance learning. IET Image Process. 2018;12:320-8.
154. Soltanian-Zadeh H, Rafiee-Rad F, Pourabdollah-Nejad DS. Comparison of multiwavelet, wavelet, Haralick, and shape features for microcalcification classification in mammograms. Pattern Recogn. 2004;37:1973-86.
155. Felipe JC, Ribeiro MX, Sousa EP, Traina AJ, Traina CJ. Effective shape-based retrieval and classification of mammograms. In Proceedings of the 2006 ACM symposium on Applied computing; 2006, pp. 250-5.
156. Soltanian-Zadeh H, Pourabdollah-Nezhad S, Rad FR. Shape-based and texture-based feature extraction for classification of microcalcifications in mammograms. Medical Imaging; 2001.
157. Zyout I, Abdel-Qader I, Jacobs C. Embedded feature selection using PSO-kNN: shape-based diagnosis of microcalcification clusters in mammography. J Ubiquitous Syst Pervasive Netw. 2011;3:7-11.
158. Sahiner B, Chan HP, Petrick N, Helvie MA, Hadjiiski LM. Improvement of mammographic mass characterization using spiculation meausures and morphological features. Med Phys. 2001;28:1455-65.
159. Haindl M, Remeš V. Pseudocolor enhancement of mammogram texture abnormalities. Mach Vision Appl. 2019;30:785-94.
160. Tai SC, Chen ZS, Tsai WT. An automatic mass detection system in mammograms based on complex texture features. IEEE J Biomed Health Inform. 2014;18:618-27.
161. Mudigonda NR, Rangayyan RM, Desautels JE. Detection of breast masses in mammograms by density slicing and texture flow-field analysis. IEEE Trans Med Imaging. 2001;20:1215-27.
162. Farhan AH, Kamil MY. Texture analysis of mammogram using local binary pattern method. J Phys Conf Ser. 2020;1530:012091.
163. Quintanilla-Domínguez J, Barrón-Adame JM, Gordillo-Sosa JA, Lozano-Garcia JM, Estrada-García H, Guzmán-Cabrera R. Analysis of mammograms using texture segmentation. Adv Lang Knowl Eng. 2016:119-26. Available from: https://www.rcs.cic.ipn.mx/2016_123/Analysis%20of%20Mammograms%20using%20Texture%20Segmentation.pdf [Last accessed on 26 Nov 2025].
164. Suckling J, Parker J, Dance D, et al. Mammographic image analysis society (MIAS) database v1.21; Apollo: University of Cambridge Repository; 2015.
165. Hung CL, Lin CY. GPU-based texture analysis approach for mammograms institute of biomedical informatics. In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Seoul, South Korea; 16-19 December 2020.
166. Bay H, Tuytelaars T, Van Gool L. SURF: speeded up robust features. In: Leonardis A, Bischof H, Pinz A, editors. Computer vision - ECCV 2006. Berlin: Springer Berlin Heidelberg; 2006. pp. 404-17.
167. Lowe DG. Object recognition from local scale-invariant features. In Proceedings of the seventh IEEE international conference on computer vision. Kerkyra, Greece; 20-27 September 1999.
168. Li J, Allinson NM. A comprehensive review of current local features for computer vision. Neurocomputing. 2008;71:1771-87.
169. Jiang M, Zhang S, Li H, Metaxas DN. Computer-aided diagnosis of mammographic masses using scalable image retrieval. IEEE Trans Biomed Eng. 2015;62:783-92.
170. Guan Q, Zhang J, Chen S, Todd-Pokropek A. Automatic segmentation of micro-calcification based on sift in mammograms. In 2008 International Conference on BioMedical Engineering and Informatics. Sanya, China; 27-30 May 2008.
171. Insalaco M, Bruno A, Farruggia A, Vitabile S, Ardizzone E. An unsupervised method for suspicious regions detection in mammogram images. In Proceedings of the International Conference on Pattern Recognition Applications and Methods. 2015; pp. 302-8.
172. Utomo A, Juniawan EF, Lioe V, Santika DD. Local features based deep learning for mammographic image classification: in comparison to CNN models. Proc Comput Sci. 2021;179:169-76.
173. Mridha MF, Hamid MA, Monowar MM, et al. A comprehensive survey on deep-learning-based breast cancer diagnosis. Cancers. 2021;13:6116.
174. Salazar-Licea A, Mendoza C, Aceves M, Pedraza J, Pastrana-Palma A. Automatic segmentation of mammograms using a Scale-Invariant Feature Transform and K-means clustering algorithm. In 2014 11th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE). Ciudad del Carmen, Mexico; 29 September 2014 - 03 October 2014.
175. Merckel LG, Bartels LW, Köhler MO, et al. MR-guided high-intensity focused ultrasound ablation of breast cancer with a dedicated breast platform. Cardiovasc Intervent Radiol. 2013;36:292-301.
