58
Views
24
CrossRef citations to date
0
Altmetric
Articles

Deep learning with perspective modeling for early detection of malignancy in mammograms

, &

References

  • http://www.wcrf.org/cancer_statistics/world_cancer_statistics.php
  • ttp://www.breastcancer.org/symptoms/understand_bc/statistics
  • http://www.worldwidebreastcancer.com
  • http://ww5.komen.org/BreastCancer/Statistics.html#worldwar
  • K. Ganesan, U. R. Acharya, C. K. Chua, L. C. Min, K. . Abraham, and K. Ng, “Computer-aided breast cancer detection using mammograms: a review,” IEEE Reviews in Biomedical Engineering, vol. 6, pp. 77–98, 2013. doi: 10.1109/RBME.2012.2232289
  • World Cancer Report, Lyon, France. http://globocan.arc.fr/fact-sheets/populations/factsheet.asp?uno=900
  • Dillon D, Guidi AJ, Schnitt SJ. Chapter 25: Pathology of Invasive Breast Cancer, in Harris JR, Lippman ME, Morrow M, Osborne CK. Diseases of the Breast, 5th edition, Lippincott Williams & Wilkins, 2014.
  • https://ww5.komen.org/BreastCancer/BreastFactsReferences.html
  • http://timesofindia.indiatimes.com/life-style/health-fitness/health/Cancer-incidence-to-rise-five-fold-in-India-by-2025/article-show/29823316.cms
  • http://ww5.komen.org/BreastCancer/Statistics.html#worldwar
  • C. Venanzi, A. Bergamaschi, F. Bruni, D. Dreossi, R. Longo, A. Olivo, S. Pani, E. Castelli, A digital detector for breast computed tomography at the SYRMEP beam line, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 548 (2005) 264–268. doi: 10.1016/j.nima.2005.03.100
  • http://www.worldwidebreastcancer.com
  • J. ang, R. Rangayyan, J. Xu, I. Naqa, and Y. Yang, “Computer-aided detection and diagnosis of breast cancer with mammography: Recent advances,” IEEE rans. Inf. echnol. Biomed., vol. 13, no. 2, pp. 236– 251,Mar. 2009. doi: 10.1109/TITB.2008.2009441
  • R. M. Rangayyan, F. Ayres, E. L. Desautels, “A review of computer-aided diagnosis of breast cancer: toward the detection of early signs,” J. Franklin Inst., vol. 344, 2007, pp. 312-348. doi: 10.1016/j.jfranklin.2006.09.003
  • S. Lahmiri and M. Boukadoum, “DW and R based approach for feature extraction and classification of mammograms with SVM,” in 2011 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2011, pp. 412–415.
  • Mudigonda, Naga R., R. Rangayyan, and JE Leo Desautels. “Gradient and texture analysis for the classification of mammographic masses.” Medical Imaging, IEEE ransactions on 19.10 (2000): 10321043
  • M. F. Akay, “Support vector machines combined with feature selection for breast cancer diagnosis,” Expert Syst. Appl., vol. 36, no. 2, pp. 3240–3247, Mar. 2009. doi: 10.1016/j.eswa.2008.01.009
  • Y. U. Ryu, R. Chandrasekaran, and V. S. Jacob, “Breast cancer prediction using the isotonic separation technique,” Eur. J. Oper. Res.,vol. 181, no. 2, pp. 842–854, Sep. 2007. doi: 10.1016/j.ejor.2006.06.031
  • S. Sahan, K. Polat, H. Kodaz, and S. Güneş, “A new hybrid method based on fuzzy-artificial immune system and k-nn algorithm for breast cancer diagnosis.,” Comput. Biol. Med., vol. 37, no. 3, pp. 415–23, Mar.2007. doi: 10.1016/j.compbiomed.2006.05.003
  • E. D. Übeyli, “Implementing automated diagnostic systems for breast cancer detection,” Expert Syst. Appl., vol. 33, no. 4, pp. 1054–1062, Nov. 2007. doi: 10.1016/j.eswa.2006.08.005
  • Yuanjiao MA, Ziwu WANG, Jeffrey Lian LU, Gang WANG, Peng LI Ianxin MA, Yinfu XIE, Zhijie ZHENG “Extracting Microcalcification Clusters on Mammograms for Early Breast Cancer Detection” Proceedings of the 2006 IEEE International Conference on Information Acquisition August 20-23, 2006, Weihai, Shandong, China, pp499–504.
  • Heng-Da Cheng, Yui Man Lui, and Rita I. Freimanis “A Novel Approach to microcalcification Detection Using Fuzzy Logic echnique” IEEE transactions on medical imaging, vol. 17, no. 3, June 1998, pp 442–450. doi: 10.1109/42.712133
  • Azra Alizad, Mostafa Fatemi, Member, Lester E. Wold, and James F. Greenleaf, “Performance of Vibro-Acoustography in Detecting Microcalcifications in Excised Human Breast issue: A Study of 74 issue Samples” IEEE transactions on medical imaging, vol. 23, no. 3, march 2004 , pp 307-312. doi: 10.1109/TMI.2004.824241
  • Anna N. Karahaliou, Ioannis S. Boniatis, Spyros G. Skiadopoulos, Filippos N. akellaropoulos, Nikolaos S. Arikidis, Eleni A. Likaki, George S. Panayiotakis, and Lena I. Costaridou “Breast Cancer Diagnosis: Analyzing exture of issue Surrounding Microcalcifications” IEEE transactions on information technology in biomedicine, vol. 12, no. 6, November 2008 , pp 731-738.
  • Al Mutaz M, Abdalla, Safaai Deris, Nazar Zaki and Doaa M. Ghoneim “Breast Cancer Detection Based on Statistical extural Features Classification” 2008 IEEE , pp 728-730.
  • D. Buller, A. Buller, P. R. Innocent, and W. Pawlak, “Determining and classifying the region of interest in ultrasonic images of the breast using neural networks,” Artificial Intelligence in Medicine, vol. 8, no.1, pp.53–66, 1996. doi: 10.1016/0933-3657(95)00020-8
  • C. Ruggierol, F. Bagnolil, R. Sacilel, M. Calabrese, G. Rescinito, and F. Sardanelli, “Automatic recognition of malignant lesions in ultrasound images by artificial neural networks,” in Proceedings of the20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 2, pp.872–875, Hong Kong, November 1998.
  • A. E. Hassanien, N. El-Bendary, M. Kudelka, and V. Snasel, ”Breast cancer detection and classification using support vector machines and pulse coupled neural network,” in Proceedings of the hird International Conference on Intelligent Human Computer Interaction (IHCI 2011), Prague, Czech Republic, August, 2011, vol.179 of Advances in Intelligent Systems and Computing , pp. 269–279, Springer, Berlin, Germany, 2013.
  • L. Chen-Chung,. Chung-Yen, a-Shan, and Y. Shyr-Shen, “An improved GVF snake based breast region extrapolation scheme for digital mammograms,” Expert Systems Applicat., vol. 39, no. 4, pp. 4505–4510, Mar. 2012, 10.1016/j.eswa.2011.09.136, 0957-4174.
  • K. Doi, H. MacMahon, S. Katsuragawa, R. M. Nishikawa, and Y. Jiang, “Computer-aided diagnosis in radiology: Potential and pitfalls,” Eur. J. Radiol., vol. 31, no. 2, pp. 97–109, Aug. 1999, 10.1016/S0720-048X(99)00016-9, 0720-048X. doi: 10.1016/S0720-048X(99)00016-9
  • Nan-Chyuan, C. Hong-Wei, and H. Sheng-Liang, “Computer-aided diagnosis for early-stage breast cancer by using wavelet transform,” Computerized Med. Imag. Graphics, vol. 35, no. 1, pp. 1–8, Jan. 2011, 10.1016/j.compmedimag.2010.08.005, 0895-6111.
  • K. Doi, “Overview on research and development of computer-aided diagnostic schemes,” Seminars in Ultrasound, C, and MRI, vol. 25, no. 5, pp. 404–410, Oct. 2004, 10 .1053/j.sult.2004.02.006, 0887-2171.
  • “Computer-Aided detection (CAD) in screening mammography: Sensitivity of commercial CAD systems for detecting architectural distortion,” AJR, vol. 181, pp. 1083–1088, October 2003.
  • K. Doi, “Computer-aided diagnosis in medical imaging: Historical review, current status and future potential,” Computerized Med. Imag. Graphics, vol. 31, no. 4–5, pp. 198–211, Jun.–Jul. 2007, 10.1016/j. compmedimag.2007.02.002, 0895-6111. doi: 10.1016/j.compmedimag.2007.02.002
  • Dezső Ribli, Anna Horváth, Zsuzsa Unger, Péter Pollner & István Csabai”, Detecting and classifying lesions in mammograms with Deep Learning”, Scientific Reports,volume 8, Article number: 4165 (2018), 15 March 2018,doi:10.1038/s41598-018-22437-z.
  • Meena, Gaurav, and Sarika Choudhary. “Biometric authentication in internet of things: A conceptual view.” Journal of Statistics and Management Systems 22.4 (2019): 643-652. doi: 10.1080/09720510.2019.1609722
  • Panov, Vladimir G., and Julia V. Nagrebetskaya. “Classification of combined action of binary factors and Coxeter groups.” Journal of Discrete Mathematical Sciences and Cryptography 21.3 (2018): 661-677. doi: 10.1080/09720529.2016.1222733
  • Rahmani, A., and S. A. MirHassani. “An improved multi-parametric method for solving MIBLPP.” Journal of Information and Optimization Sciences 39.6 (2018): 1309-1328. doi: 10.1080/02522667.2017.1367508
  • Aly A. Mohamed, Wendie A. Berg, Hong Peng, Yahong Luo, Rachel C. Jankowitz, Shandong Wu, “A deep learning method for classifying mammographic breast density categories”, Med Phys, 2018 Jan; 45(1): 314–321,doi: 10.1002/mp.12683.
  • Syed Jamal Safdar Gardezi ; Muhammad Awais ; Ibrahima Faye; Fabrice Meriaudeau,” Mammogram classification using deep learning features”, 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), DOI: 10.1109/ICSI-PA.2017.8120660.
  • S. Sahan, K. Polat, H. Kodaz, and S. Güneş, “A new hybrid method based on fuzzy-artificial immune system and k-nn algorithm for breast cancer diagnosis.,” Comput. Biol. Med., vol. 37, no. 3, pp. 415– 23, Mar.2007. doi: 10.1016/j.compbiomed.2006.05.003
  • C. H. Lee, D. D. Dershaw, D. Kopans, P. Evans, B. Monsees, D. Monticciolo, R. J. Brenner, L. Bassett, W. Berg, and S. Feig, “Breast cancer screening with imaging: Recommendations from the Society of Breast Imaging and the ACR on the use of mammography, breast MRI, breast ultrasound, and other technologies for the detection of clinically occult breast cancer,” J. Amer. Coll. Radiol., vol. 7, no. 1, pp. 18–27, Jan. 2010. doi: 10.1016/j.jacr.2009.09.022
  • he Mammographic Image Analysis Society Digital Mammogram Database Exerpta Medica. International Congress Series 1069 pp375-378. http://peipa.essex.ac.uk/info/mias.html
  • Dezső Ribli, Anna Horváth, Zsuzsa Unger, Péter Pollner & István Csabai, “Detecting and classifying lesions in mammograms with Deep Learning”, Scientific Reports,volume 8, Article number: 4165 (2018), 15 March 2018, doi:10.1038/s41598-018-22437-z.
  • Cruz-Roa, Angel et al. “Accurate and Reproducible Invasive Breast Cancer Detection in Whole-Slide Images: A Deep Learning Approach for Quantifying umor Extent.” Scientific Reports 7 (2017): 46450. PMC. Web. 26 Sept. 2018.
  • Xiaofei Zhang, Yi Zhang, Erik Y. Han, Nathan Jacobs, Qiong Han, Xiaoqin Wang, Jinze Liu, “Classification of Whole Mammogram and omosynthesis Images Using Deep Convolutional Neural Networks”, NanoBioscience IEEE ransactions, vol. 17, no. 3, pp. 237-242, 2018. doi: 10.1109/TNB.2018.2845103
  • Geert Litjens, Bramvan Ginneken, Albert Gubern-Mérida, Clara I. Sánchez, Ritse Mann, Ardden Heeten, Nico Karssemeije,” Large scale deep learning for computer aided detection of mammographic lesions”, Elsevier Publisher, Medical image analysis, ISSN: 1361-8423, Vol: 35, Page: 303-312,2017.
  • Aly A. Mohamed, Wendie A. Berg, Hong Peng, Yahong Luo, Rachel C. Jankowitz, Shandong Wu, “A deep learning method for classifying mammographic breast density categories”, Med Phys, 2018 Jan; 45(1): 314–321,doi: 10.1002/mp.12683.
  • Syed Jamal Safdar Gardezi; Muhammad Awais; Ibrahima Faye; Fabrice Meriaudeau,” Mammogram classification using deep learning features”, 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), DOI: 10.1109/ICSIPA.2017.8120660.
  • M. Mohsin Jadoon, Qianni Zhang, Ihsan Ul Haq, Sharjeel Butt and Adeel Jadoon, “ hree-Class Mammogram Classification Based on Descriptive CNN Features”, Hindawi BioMed Research International, Volume 2017, Article ID 3640901, January 2017.
  • Indrajeet Kumar, Bhadauria H.S, Jitendra Virmani, Shruti hakur,” A classification framework for prediction of breast density using an ensemble of neural network classifiers”, Biocybernetics and Biomedical Engineering, Volume 37, Issue 1, 2017, Pages 217-228.
  • Qiu Y., et al. Computer-aided classification of mammographic masses using the deep learning technology: a preliminary study. in SPIE, Medical Imaging 2016: Computer-Aided Diagnosis 9785, (2016).
  • Choi J. Y., Kim D. H., Plataniotis K. N. & Ro Y. M. Classifier ensemble generation and selection with multiple feature representations for classification applications in computer-aided detection and diagnosis on mammography. Expert Syst. Appl. 46, 106–121 (2016).
  • C. Abirami, R. Harikumar, and S. Chakravarthy, “Performance analysis and detection of micro calcification in digital mammograms using wavelet features,” in Proceedings of the International Conference on Wireless Communications, Signal Processing and Networking (WiSPNE ‘16), pp. 2327–2331, Chennai, India, March 2016.
  • Uppal and M. Naseem, “Classification of mammograms for breast cancer detection using fusion of discrete cosine transform and discrete wavelet transform features,” Biomedical Research, In press.
  • Chu J., Min H., Liu L. & Lu W. A novel computer aided breast mass detection scheme based on morphological enhancement and SLIC superpixel segmentation. Med. Phys. 42, 3859–3869 (2015). doi: 10.1118/1.4921612
  • Görgel P., Sertbas A. & Uçan O. N. Computer-aided classification of breast masses in mammogram images based on spherical wavelet transform and support vector machines. Expert Syst 32, 155–164 (2015). doi: 10.1111/exsy.12073
  • Rebecca Sawyer Lee, Francisco Gimenez, Assaf Hoogi, Kanae Kawai Miyake, Mia Gorovoy and Daniel L. Rubinb, “A curated mammography data set for use in computer-aided detection and diagnosis research”, Scientific Data, doi: 10.1038/sdata.2017.177, 2017.
  • Rebecca Sawyer Lee, Francisco Gimenez, Assaf Hoogi, Daniel Rubin (2016). Curated Breast Imaging Subset of DDSM. he Cancer Imaging Archive. http://dx.doi.org/10.7937/K9/CIA.2016.7O02S9CY.
  • Rebecca Sawyer Lee, Francisco Gimenez, Assaf Hoogi, Kanae Kawai Miyake, Mia Gorovoy & Daniel L. Rubin. A curated mammography data set for use in computer-aided detection and diagnosis research. Scientific Data volume 4, Article number: 170177 (2017).
  • Berta M. Geller EdD, Jane ZapkaScD, Solveig S.-H. HofvindMSc, AstridScharpantgen RN, MPH, Livia Giordano MD, Noriaki Ohuchi MD PhD &Rachel Ballard-Barbash MD MPH. Communicating with Women About Mammography, Journal of Cancer Education,Volume 22, 2007 - Issue 1
  • Laxman Singh & Zainul Abdin Jaffery, Computerized detection of breast cancer in digital mammograms, International Journal of Computers and Applications,Volume 40, 2018 - Issue 2
  • Shiraz I. Mishra MBBS PhD, Roshan BastaniPhD, David Huang DRPH, Pat H. Luce MS & Claudia R. Baquet MD MPH, Mammography Screening and Pacific Islanders: Role of Cultural and Psychosocial Factors, Journal of Cancer Education, Volume 22, 2007 - Issue 1

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.