Deep-Learning-Based Computer- Aided Systems for Breast Cancer Imaging: A Critical Review

[EN] This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis/detection (CAD) systems, which make use of new deep learning methods to automatically recognize breast images and improve the accuracy of diagnoses made by radiologists. This review is based upon published literature in the past decade (January 2010-January 2020), where we obtained around 250 research articles, and after an eligibility process, 59 articles were presented in more detail. The main findings in the classification process revealed that new DL-CAD methods are useful and effective screening tools for breast cancer, thus reducing the need for manual feature extraction. The breast tumor research community can utilize this survey as a basis for their current and future studies.This project has been co-financed by the Spanish Government Grant PID2019-107790RB-C22, "Software development for a continuous PET crystal systems applied to breast cancer".Jiménez-Gaona, Y.; Rodríguez Álvarez, MJ.; Lakshminarayanan, V. (2020). Deep-Learning-Based Computer- Aided Systems for Breast Cancer Imaging: A Critical Review. Applied Sciences. 10(22):1-29. https://doi.org/10.3390/app10228298S1291022Jemal, A., Bray, F., Center, M. M., Ferlay, J., Ward, E., & Forman, D. (2011). Global cancer statistics. CA: A Cancer Journal for Clinicians, 61(2), 69-90. doi:10.3322/caac.20107Gao, F., Chia, K.-S., Ng, F.-C., Ng, E.-H., & Machin, D. (2002). Interval cancers following breast cancer screening in Singaporean women. International Journal of Cancer, 101(5), 475-479. doi:10.1002/ijc.10636Munir, K., Elahi, H., Ayub, A., Frezza, F., & Rizzi, A. (2019). Cancer Diagnosis Using Deep Learning: A Bibliographic Review. Cancers, 11(9), 1235. doi:10.3390/cancers11091235Nahid, A.-A., & Kong, Y. (2017). Involvement of Machine Learning for Breast Cancer Image Classification: A Survey. Computational and Mathematical Methods in Medicine, 2017, 1-29. doi:10.1155/2017/3781951Ramadan, S. Z. (2020). Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review. Journal of Healthcare Engineering, 2020, 1-21. doi:10.1155/2020/9162464CHAN, H.-P., DOI, K., VYBRONY, C. J., SCHMIDT, R. A., METZ, C. E., LAM, K. L., … MACMAHON, H. (1990). Improvement in Radiologists?? Detection of Clustered Microcalcifications on Mammograms. Investigative Radiology, 25(10), 1102-1110. doi:10.1097/00004424-199010000-00006Olsen, O., & Gøtzsche, P. C. (2001). Cochrane review on screening for breast cancer with mammography. The Lancet, 358(9290), 1340-1342. doi:10.1016/s0140-6736(01)06449-2Mann, R. M., Kuhl, C. K., Kinkel, K., & Boetes, C. (2008). Breast MRI: guidelines from the European Society of Breast Imaging. European Radiology, 18(7), 1307-1318. doi:10.1007/s00330-008-0863-7Jalalian, A., Mashohor, S. B. T., Mahmud, H. R., Saripan, M. I. B., Ramli, A. R. B., & Karasfi, B. (2013). Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. Clinical Imaging, 37(3), 420-426. doi:10.1016/j.clinimag.2012.09.024Sarno, A., Mettivier, G., & Russo, P. (2015). Dedicated breast computed tomography: Basic aspects. Medical Physics, 42(6Part1), 2786-2804. doi:10.1118/1.4919441Njor, S., Nyström, L., Moss, S., Paci, E., Broeders, M., Segnan, N., & Lynge, E. (2012). Breast Cancer Mortality in Mammographic Screening in Europe: A Review of Incidence-Based Mortality Studies. Journal of Medical Screening, 19(1_suppl), 33-41. doi:10.1258/jms.2012.012080Morrell, S., Taylor, R., Roder, D., & Dobson, A. (2012). Mammography screening and breast cancer mortality in Australia: an aggregate cohort study. Journal of Medical Screening, 19(1), 26-34. doi:10.1258/jms.2012.011127Marmot, M. G., Altman, D. G., Cameron, D. A., Dewar, J. A., Thompson, S. G., & Wilcox, M. (2013). The benefits and harms of breast cancer screening: an independent review. British Journal of Cancer, 108(11), 2205-2240. doi:10.1038/bjc.2013.177Pisano, E. D., Gatsonis, C., Hendrick, E., Yaffe, M., Baum, J. K., Acharyya, S., … Rebner, M. (2005). Diagnostic Performance of Digital versus Film Mammography for Breast-Cancer Screening. New England Journal of Medicine, 353(17), 1773-1783. doi:10.1056/nejmoa052911Carney, P. A., Miglioretti, D. L., Yankaskas, B. C., Kerlikowske, K., Rosenberg, R., Rutter, C. M., … Ballard-Barbash, R. (2003). Individual and Combined Effects of Age, Breast Density, and Hormone Replacement Therapy Use on the Accuracy of Screening Mammography. Annals of Internal Medicine, 138(3), 168. doi:10.7326/0003-4819-138-3-200302040-00008Woodard, D. B., Gelfand, A. E., Barlow, W. E., & Elmore, J. G. (2007). Performance assessment for radiologists interpreting screening mammography. Statistics in Medicine, 26(7), 1532-1551. doi:10.1002/sim.2633Cole, E. B., Pisano, E. D., Kistner, E. O., Muller, K. E., Brown, M. E., Feig, S. A., … Braeuning, M. P. (2003). Diagnostic Accuracy of Digital Mammography in Patients with Dense Breasts Who Underwent Problem-solving Mammography: Effects of Image Processing and Lesion Type. Radiology, 226(1), 153-160. doi:10.1148/radiol.2261012024Boyd, N. F., Guo, H., Martin, L. J., Sun, L., Stone, J., Fishell, E., … Yaffe, M. J. (2007). Mammographic Density and the Risk and Detection of Breast Cancer. New England Journal of Medicine, 356(3), 227-236. doi:10.1056/nejmoa062790Bird, R. E., Wallace, T. W., & Yankaskas, B. C. (1992). Analysis of cancers missed at screening mammography. Radiology, 184(3), 613-617. doi:10.1148/radiology.184.3.1509041Kerlikowske, K. (2000). Performance of Screening Mammography among Women with and without a First-Degree Relative with Breast Cancer. Annals of Internal Medicine, 133(11), 855. doi:10.7326/0003-4819-133-11-200012050-00009Nunes, F. L. S., Schiabel, H., & Goes, C. E. (2006). Contrast Enhancement in Dense Breast Images to Aid Clustered Microcalcifications Detection. Journal of Digital Imaging, 20(1), 53-66. doi:10.1007/s10278-005-6976-5Dinnes, J., Moss, S., Melia, J., Blanks, R., Song, F., & Kleijnen, J. (2001). Effectiveness and cost-effectiveness of double reading of mammograms in breast cancer screening: findings of a systematic review. The Breast, 10(6), 455-463. doi:10.1054/brst.2001.0350Robinson, P. J. (1997). Radiology’s Achilles’ heel: error and variation in the interpretation of the Röntgen image. The British Journal of Radiology, 70(839), 1085-1098. doi:10.1259/bjr.70.839.9536897Rangayyan, R. M., Ayres, F. J., & Leo Desautels, J. E. (2007). A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs. Journal of the Franklin Institute, 344(3-4), 312-348. doi:10.1016/j.jfranklin.2006.09.003Vyborny, C. J., Giger, M. L., & Nishikawa, R. M. (2000). COMPUTER-AIDED DETECTION AND DIAGNOSIS OF BREAST CANCER. Radiologic Clinics of North America, 38(4), 725-740. doi:10.1016/s0033-8389(05)70197-4Giger, M. L. (2018). Machine Learning in Medical Imaging. Journal of the American College of Radiology, 15(3), 512-520. doi:10.1016/j.jacr.2017.12.028Xu, Y., Wang, Y., Yuan, J., Cheng, Q., Wang, X., & Carson, P. L. (2019). Medical breast ultrasound image segmentation by machine learning. Ultrasonics, 91, 1-9. doi:10.1016/j.ultras.2018.07.006Shan, J., Alam, S. K., Garra, B., Zhang, Y., & Ahmed, T. (2016). Computer-Aided Diagnosis for Breast Ultrasound Using Computerized BI-RADS Features and Machine Learning Methods. Ultrasound in Medicine & Biology, 42(4), 980-988. doi:10.1016/j.ultrasmedbio.2015.11.016Zhang, Q., Xiao, Y., Dai, W., Suo, J., Wang, C., Shi, J., & Zheng, H. (2016). Deep learning based classification of breast tumors with shear-wave elastography. Ultrasonics, 72, 150-157. doi:10.1016/j.ultras.2016.08.004Cheng, J.-Z., Ni, D., Chou, Y.-H., Qin, J., Tiu, C.-M., Chang, Y.-C., … Chen, C.-M. (2016). Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans. Scientific Reports, 6(1). doi:10.1038/srep24454Shin, S. Y., Lee, S., Yun, I. D., Kim, S. M., & Lee, K. M. (2019). Joint Weakly and Semi-Supervised Deep Learning for Localization and Classification of Masses in Breast Ultrasound Images. IEEE Transactions on Medical Imaging, 38(3), 762-774. doi:10.1109/tmi.2018.2872031Wang, J., Ding, H., Bidgoli, F. A., Zhou, B., Iribarren, C., Molloi, S., & Baldi, P. (2017). Detecting Cardiovascular Disease from Mammograms With Deep Learning. IEEE Transactions on Medical Imaging, 36(5), 1172-1181. doi:10.1109/tmi.2017.2655486Kooi, T., Litjens, G., van Ginneken, B., Gubern-Mérida, A., Sánchez, C. I., Mann, R., … Karssemeijer, N. (2017). Large scale deep learning for computer aided detection of mammographic lesions. Medical Image Analysis, 35, 303-312. doi:10.1016/j.media.2016.07.007Debelee, T. G., Schwenker, F., Ibenthal, A., & Yohannes, D. (2019). Survey of deep learning in breast cancer image analysis. Evolving Systems, 11(1), 143-163. doi:10.1007/s12530-019-09297-2Keen, J. D., Keen, J. M., & Keen, J. E. (2018). Utilization of Computer-Aided Detection for Digital Screening Mammography in the United States, 2008 to 2016. Journal of the American College of Radiology, 15(1), 44-48. doi:10.1016/j.jacr.2017.08.033Henriksen, E. L., Carlsen, J. F., Vejborg, I. M., Nielsen, M. B., & Lauridsen, C. A. (2018). The efficacy of using computer-aided detection (CAD) for detection of breast cancer in mammography screening: a systematic review. Acta Radiologica, 60(1), 13-18. doi:10.1177/0284185118770917Gao, Y., Geras, K. J., Lewin, A. A., & Moy, L. (2019). New Frontiers: An Update on Computer-Aided Diagnosis for Breast Imaging in the Age of Artificial Intelligence. American Journal of Roentgenology, 212(2), 300-307. doi:10.2214/ajr.18.20392Pacilè, S., Lopez, J., Chone, P., Bertinotti, T., Grouin, J. M., & Fillard, P. (2020). Improving Breast Cancer Detection Accuracy of Mammography with the Concurrent Use of an Artificial Intelligence Tool. Radiology: Artificial Intelligence, 2(6), e190208. doi:10.1148/ryai.2020190208Huynh, B. Q., Li, H., & Giger, M. L. (2016). Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. Journal of Medical Imaging, 3(3), 034501. doi:10.1117/1.jmi.3.3.034501Yap, M. H., Pons, G., Marti, J., Ganau, S., Sentis, M., Zwiggelaar, R., … Marti, R. (2018). Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks. IEEE Journal of Biomedical and Health Informatics, 22(4), 1218-1226. doi:10.1109/jbhi.2017.2731873Moon, W. K., Lee, Y.-W., Ke, H.-H., Lee, S. H., Huang, C.-S., & Chang, R.-F. (2020). Computer‐aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks. Computer Methods and Programs in Biomedicine, 190, 105361. doi:10.1016/j.cmpb.2020.105361LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. doi:10.1038/nature14539Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2017). Deep learning for healthcare: review, opportunities and challenges. Briefings in Bioinformatics, 19(6), 1236-1246. doi:10.1093/bib/bbx044Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., … Summers, R. M. (2016). Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning. IEEE Transactions on Medical Imaging, 35(5), 1285-1298. doi:10.1109/tmi.2016.2528162Lee, J.-G., Jun, S., Cho, Y.-W., Lee, H., Kim, G. B., Seo, J. B., & Kim, N. (2017). Deep Learning in Medical Imaging: General Overview. Korean Journal of Radiology, 18(4), 570. doi:10.3348/kjr.2017.18.4.570Suzuki, K. (2017). Overview of deep learning in medical imaging. Radiological Physics and Technology, 10(3), 257-273. doi:10.1007/s12194-017-0406-5Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2010). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. International Journal of Surgery, 8(5), 336-341. doi:10.1016/j.ijsu.2010.02.007Khan, K. S., Kunz, R., Kleijnen, J., & Antes, G. (2003). Five Steps to Conducting a Systematic Review. Journal of the Royal Society of Medicine, 96(3), 118-121. doi:10.1177/014107680309600304Han, S., Kang, H.-K., Jeong, J.-Y., Park, M.-H., Kim, W., Bang, W.-C., & Seong, Y.-K. (2017). A deep learning framework for supporting the classification of breast lesions in ultrasound images. Physics in Medicine & Biology, 62(19), 7714-7728. doi:10.1088/1361-6560/aa82ecMoreira, I. C., Amaral, I., Domingues, I., Cardoso, A., Cardoso, M. J., & Cardoso, J. S. (2012). INbreast. Academic Radiology, 19(2), 236-248. doi:10.1016/j.acra.2011.09.014Abdelhafiz, D., Yang, C., Ammar, R., & Nabavi, S. (2019). Deep convolutional neural networks for mammography: advances, challenges and applications. BMC Bioinformatics, 20(S11). doi:10.1186/s12859-019-2823-4Byra, M., Jarosik, P., Szubert, A., Galperin, M., Ojeda-Fournier, H., Olson, L., … Andre, M. (2020). Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network. Biomedical Signal Processing and Control, 61, 102027. doi:10.1016/j.bspc.2020.102027Jiao, Z., Gao, X., Wang, Y., & Li, J. (2016). A deep feature based framework for breast masses classification. Neurocomputing, 197, 221-231. doi:10.1016/j.neucom.2016.02.060Arevalo, J., González, F. A., Ramos-Pollán, R., Oliveira, J. L., & Guevara Lopez, M. A. (2016). Representation learning for mammography mass lesion classification with convolutional neural networks. Computer Methods and Programs in Biomedicine, 127, 248-257. doi:10.1016/j.cmpb.2015.12.014Peng, W., Mayorga, R. V., & Hussein, E. M. A. (2016). An automated confirmatory system for analysis of mammograms. Computer Methods and Programs in Biomedicine, 125, 134-144. doi:10.1016/j.cmpb.2015.09.019Al-Dhabyani, W., Gomaa, M., Khaled, H., & Fahmy, A. (2020). Dataset of breast ultrasound images. Data in Brief, 28, 104863. doi:10.1016/j.dib.2019.104863Piotrzkowska-Wróblewska, H., Dobruch-Sobczak, K., Byra, M., & Nowicki, A. (2017). Open access database of raw ultrasonic signals acquired from malignant and benign breast lesions. Medical Physics, 44(11), 6105-6109. doi:10.1002/mp.12538Fujita, H. (2020). AI-based computer-aided diagnosis (AI-CAD): the latest review to read first. Radiological Physics and Technology, 13(1), 6-19. doi:10.1007/s12194-019-00552-4Sengupta, S., Singh, A., Leopold, H. A., Gulati, T., & Lakshminarayanan, V. (2020). Ophthalmic diagnosis using deep learning with fundus images – A critical review. Artificial Intelligence in Medicine, 102, 101758. doi:10.1016/j.artmed.2019.101758Ganesan, K., Acharya, U. R., Chua, K. C., Min, L. C., & Abraham, K. T. (2013). Pectoral muscle segmentation: A review. Computer Methods and Programs in Biomedicine, 110(1), 48-57. doi:10.1016/j.cmpb.2012.10.020Huang, Q., Luo, Y., & Zhang, Q. (2017). Breast ultrasound image segmentation: a survey. International Journal of Computer Assisted Radiology and Surgery, 12(3), 493-507. doi:10.1007/s11548-016-1513-1Noble, J. A., & Boukerroui, D. (2006). Ultrasound image segmentation: a survey. IEEE Transactions on Medical Imaging, 25(8), 987-1010. doi:10.1109/tmi.2006.877092Kallergi, M., Woods, K., Clarke, L. P., Qian, W., & Clark, R. A. (1992). Image segmentation in digital mammography: Comparison of local thresholding and region growing algorithms. Computerized Medical Imaging and Graphics, 16(5), 323-331. doi:10.1016/0895-6111(92)90145-yTsantis, S., Dimitropoulos, N., Cavouras, D., & Nikiforidis, G. (2006). A hybrid multi-scale model for thyroid nodule boundary detection on ultrasound images. Computer Methods and Programs in Biomedicine, 84(2-3), 86-98. doi:10.1016/j.cmpb.2006.09.006Ilesanmi, A. E., Idowu, O. P., & Makhanov, S. S. (2020). Multiscale superpixel method for segmentation of breast ultrasound. Computers in Biology and Medicine, 125, 103879. doi:10.1016/j.compbiomed.2020.103879Chen, D.-R., Chang, R.-F., Kuo, W.-J., Chen, M.-C., & Huang, Y. .-L. (2002). Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks. Ultrasound in Medicine & Biology, 28(10), 1301-1310. doi:10.1016/s0301-5629(02)00620-8Cheng, H. D., Shan, J., Ju, W., Guo, Y., & Zhang, L. (2010). Automated breast cancer detection and classification using ultrasound images: A survey. Pattern Recognition, 43(1), 299-317. doi:10.1016/j.patcog.2009.05.012Chan, H.-P., Wei, D., Helvie, M. A., Sahiner, B., Adler, D. D., Goodsitt, M. M., & Petrick, N. (1995). Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature space. Physics in Medicine and Biology, 40(5), 857-876. doi:10.1088/0031-9155/40/5/010Tanaka, T., Torii, S., Kabuta, I., Shimizu, K., & Tanaka, M. (2007). Pattern Classification of Nevus with Texture Analysis. IEEJ Transactions on Electrical and Electronic Engineering, 3(1), 143-150. doi:10.1002/tee.20246Singh, B., Jain, V. K., & Singh, S. (2014). Mammogram Mass Classification Using Support Vector Machine with Texture, Shape Features and Hierarchical Centroid Method. Journal of Medical Imaging and Health Informatics, 4(5), 687-696. doi:10.1166/jmihi.2014.1312Pal, N. R., Bhowmick, B., Patel, S. K., Pal, S., & Das, J. (2008). A multi-stage neural network aided system for detection of microcalcifications in digitized mammograms. Neurocomputing, 71(13-15), 2625-2634. doi:10.1016/j.neucom.2007.06.015Ayer, T., Chen, Q., & Burnside, E. S. (2013). Artificial Neural Networks in Mammography Interpretation and Diagnostic Decision Making. Computational and Mathematical Methods in Medicine, 2013, 1-10. doi:10.1155/2013/832509Sumbaly, R., Vishnusri, N., & Jeyalatha, S. (2014). Diagnosis of Breast Cancer using Decision Tree Data Mining Technique. International Journal of Computer Applications, 98(10), 16-24. doi:10.5120/17219-7456Landwehr, N., Hall, M., & Frank, E. (2005). Logistic Model Trees. Machine Learning, 59(1-2), 161-205. doi:10.1007/s10994-005-0466-3Abdel-Zaher, A. M., & Eldeib, A. M. (2016). Breast cancer classification using deep belief networks. Expert Systems with Applications, 46, 139-144. doi:10.1016/j.eswa.2015.10.015Nishikawa, R. M., Giger, M. L., Doi, K., Metz, C. E., Yin, F.-F., Vyborny, C. J., & Schmidt, R. A. (1994). Effect of case selection on the performance of computer-aided detection schemes. Medical Physics, 21(2), 265-269. doi:10.1118/1.597287Guo, R., Lu, G., Qin, B., & Fei, B. (2018). Ultrasound Imaging Technologies for Breast Cancer Detection and Management: A Review. Ultrasound in Medicine & Biology, 44(1), 37-70. doi:10.1016/j.ultrasmedbio.2017.09.012Kang, C.-C., Wang, W.-J., & Kang, C.-H. (2012). Image segmentation with complicated background by using seeded region growing. AEU - International Journal of Electronics and Communications, 66(9), 767-771. doi:10.1016/j.aeue.2012.01.011Prabusankarlal, K. M., Thirumoorthy, P., & Manavalan, R. (2014). Computer Aided Breast Cancer Diagnosis Techniques in Ultrasound: A Survey. Journal of Medical Imaging and Health Informatics, 4(3), 331-349. doi:10.1166/jmihi.2014.1269Abdallah, Y. M., Elgak, S., Zain, H., Rafiq, M., A. Ebaid, E., & A. Elnaema, A. (2018). Breast cancer detection using image enhancement and segmentation algorithms. Biomedical Research, 29(20). doi:10.4066/biomedicalresearch.29-18-1106K.U, S., & S, G. R. (2016). Objective Quality Assessment of Image Enhancement Methods in Digital Mammography - A Comparative Study. Signal & Image Processing : An International Journal, 7(4), 01-13. doi:10.5121/sipij.2016.7401Pizer, S. M., Amburn, E. P., Austin, J. D., Cromartie, R., Geselowitz, A., Greer, T., … Zuiderveld, K. (1987). Adaptive histogram equalization and its variations. Computer Vision, Graphics, and Image Processing, 39(3), 355-368. doi:10.1016/s0734-189x(87)80186-xPisano, E. D., Zong, S., Hemminger, B. M., DeLuca, M., Johnston, R. E., Muller, K., … Pizer, S. M. (1998). Contrast Limited Adaptive Histogram Equalization image processing to improve the detection of simulated spiculations in dense mammograms. Journal of Digital Imaging, 11(4), 193-200. doi:10.1007/bf03178082Wan, J., Yin, H., Chong, A.-X., & Liu, Z.-H. (2020). Progressive residual networks for image super-resolution. Applied Intelligence, 50(5), 1620-1632. doi:10.1007/s10489-019-01548-8Umehara, K., Ota, J., & Ishida, T. (2017). Super-Resolution Imaging of Mammograms Based on the Super-Resolution Convolutional Neural Network. Open Journal of Medical Imaging, 07(04), 180-195. doi:10.4236/ojmi.2017.74018Dong, C., Loy, C. C., He, K., & Tang, X. (2016). Image Super-Resolution Using Deep Convolutional Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(2), 295-307. doi:10.1109/tpami.2015.2439281Jiang, Y., & Li, J. (2020). Generative Adversarial Network for Image Super-Resolution Combining Texture Loss. Applied Sciences, 10(5), 1729. doi:10.3390/app10051729Schultz, R. R., & Stevenson, R. L. (1994). A Bayesian approach to image expansion for improved definition. IEEE Transactions on Image Processing, 3(3), 233-242. doi:10.1109/83.287017Lei Zhang, & Xiaolin Wu. (2006). An edge-guided image interpolation algorithm via directional filtering and data fusion. IEEE Transactions on Image Processing, 15(8), 2226-2238. doi:10.1109/tip.2006.877407Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on Image Data Augmentation for Deep Learning. Journal of Big Data, 6(1). doi:10.1186/s40537-019-0197-0Weiss, K., Khoshgoftaar, T. M., & Wang, D. (2016). A survey of transfer learning. Journal of Big Data, 3(1). doi:10.1186/s40537-016-0043-6Ling Shao, Fan Zhu, & Xuelong Li. (2015). Transfer Learning for Visual Categorization: A Survey. IEEE Transactions on Neural Networks and Learning Syste

An Efficient Automatic Mass Classification Method In Digitized Mammograms Using Artificial Neural Network

In this paper we present an efficient computer aided mass classification method in digitized mammograms using Artificial Neural Network (ANN), which performs benign-malignant classification on region of interest (ROI) that contains mass. One of the major mammographic characteristics for mass classification is texture. ANN exploits this important factor to classify the mass into benign or malignant. The statistical textural features used in characterizing the masses are mean, standard deviation, entropy, skewness, kurtosis and uniformity. The main aim of the method is to increase the effectiveness and efficiency of the classification process in an objective manner to reduce the numbers of false-positive of malignancies. Three layers artificial neural network (ANN) with seven features was proposed for classifying the marked regions into benign and malignant and 90.91% sensitivity and 83.87% specificity is achieved that is very much promising compare to the radiologist's sensitivity 75%.Comment: 13 pages, 10 figure

Combined adaptive enhancement and regionâ growing segmentation of breast masses on digitized mammograms

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134789/1/mp8658.pd

A Review of Artificial Intelligence in Breast Imaging

With the increasing dominance of artificial intelligence (AI) techniques, the important prospects for their application have extended to various medical fields, including domains such as in vitro diagnosis, intelligent rehabilitation, medical imaging, and prognosis. Breast cancer is a common malignancy that critically affects women’s physical and mental health. Early breast cancer screening—through mammography, ultrasound, or magnetic resonance imaging (MRI)—can substantially improve the prognosis for breast cancer patients. AI applications have shown excellent performance in various image recognition tasks, and their use in breast cancer screening has been explored in numerous studies. This paper introduces relevant AI techniques and their applications in the field of medical imaging of the breast (mammography and ultrasound), specifically in terms of identifying, segmenting, and classifying lesions; assessing breast cancer risk; and improving image quality. Focusing on medical imaging for breast cancer, this paper also reviews related challenges and prospects for AI

Characterization of mammographic masses based on level set segmentation with new image features and patient information

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134904/1/mp0630.pd

Mammography

In this volume, the topics are constructed from a variety of contents: the bases of mammography systems, optimization of screening mammography with reference to evidence-based research, new technologies of image acquisition and its surrounding systems, and case reports with reference to up-to-date multimodality images of breast cancer. Mammography has been lagged in the transition to digital imaging systems because of the necessity of high resolution for diagnosis. However, in the past ten years, technical improvement has resolved the difficulties and boosted new diagnostic systems. We hope that the reader will learn the essentials of mammography and will be forward-looking for the new technologies. We want to express our sincere gratitude and appreciation?to all the co-authors who have contributed their work to this volume