Abnormality Detection in Mammography using Deep Convolutional Neural Networks

Breast cancer is the most common cancer in women worldwide. The most common screening technology is mammography. To reduce the cost and workload of radiologists, we propose a computer aided detection approach for classifying and localizing calcifications and masses in mammogram images. To improve on conventional approaches, we apply deep convolutional neural networks (CNN) for automatic feature learning and classifier building. In computer-aided mammography, deep CNN classifiers cannot be trained directly on full mammogram images because of the loss of image details from resizing at input layers. Instead, our classifiers are trained on labelled image patches and then adapted to work on full mammogram images for localizing the abnormalities. State-of-the-art deep convolutional neural networks are compared on their performance of classifying the abnormalities. Experimental results indicate that VGGNet receives the best overall accuracy at 92.53\% in classifications. For localizing abnormalities, ResNet is selected for computing class activation maps because it is ready to be deployed without structural change or further training. Our approach demonstrates that deep convolutional neural network classifiers have remarkable localization capabilities despite no supervision on the location of abnormalities is provided.Comment: 6 page

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Application of Fractal and Wavelets in Microcalcification Detection

Breast cancer has been recognized as one or the most frequent, malignant tumors in women, clustered microcalcifications in mammogram images has been widely recognized as an early sign of breast cancer. This work is devote to review the application of Fractal and Wavelets in microcalcifications detection

A Decision Support System (DSS) for Breast Cancer Detection Based on Invariant Feature Extraction, Classification, and Retrieval of Masses of Mammographic Images

This paper presents an integrated system for the breast cancer detection from mammograms based on automated mass detection, classification, and retrieval with a goal to support decision-making by retrieving and displaying the relevant past cases as well as predicting the images as benign or malignant. It is hypothesized that the proposed diagnostic aid would refresh the radiologist’s mental memory to guide them to a precise diagnosis with concrete visualizations instead of only suggesting a second diagnosis like many other CAD systems. Towards achieving this goal, a Graph-Based Visual Saliency (GBVS) method is used for automatic mass detection, invariant features are extracted based on using Non-Subsampled Contourlet transform (NSCT) and eigenvalues of the Hessian matrix in a histogram of oriented gradients (HOG), and finally classification and retrieval are performed based on using Support Vector Machines (SVM) and Extreme Learning Machines (ELM), and a linear combination-based similarity fusion approach. The image retrieval and classification performances are evaluated and compared in the benchmark Digital Database for Screening Mammography (DDSM) of 2604 cases by using both the precision-recall and classification accuracies. Experimental results demonstrate the effectiveness of the proposed system and show the viability of a real-time clinical application

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

Artificial neural network and its applications in quality process control, document recognition and biomedical imaging

In computer-vision based system a digital image obtained by a digital camera would usually have 24-bit color image. The analysis of an image with that many levels might require complicated image processing techniques and higher computational costs. But in real-time application, where a part has to be inspected within a few milliseconds, either we have to reduce the image to a more manageable number of gray levels, usually two levels (binary image), and at the same time retain all necessary features of the original image or develop a complicated technique. A binary image can be obtained by thresholding the original image into two levels. Therefore, thresholding of a given image into binary image is a necessary step for most image analysis and recognition techniques. In this thesis, we have studied the effectiveness of using artificial neural network (ANN) in pharmaceutical, document recognition and biomedical imaging applications for image thresholding and classification purposes. Finally, we have developed edge-based, ANN-based and region-growing based image thresholding techniques to extract low contrast objects of interest and classify them into respective classes in those applications. Real-time quality inspection of gelatin capsules in pharmaceutical applications is an important issue from the point of view of industry\u27s productivity and competitiveness. Computer vision-based automatic quality inspection and controller system is one of the solutions to this problem. Machine vision systems provide quality control and real-time feedback for industrial processes, overcoming physical limitations and subjective judgment of humans. In this thesis, we have developed an image processing system using edge-based image thresholding techniques for quality inspection that satisfy the industrial requirements in pharmaceutical applications to pass the accepted and rejected capsules. In document recognition application, success of OCR mostly depends on the quality of the thresholded image. Non-uniform illumination, low contrast and complex background make it challenging in this application. In this thesis, optimal parameters for ANN-based local thresholding approach for gray scale composite document image with non-uniform background is proposed. An exhaustive search was conducted to select the optimal features and found that pixel value, mean and entropy are the most significant features at window size 3x3 in this application. For other applications, it might be different, but the procedure to find the optimal parameters is same. The average recognition rate 99.25% shows that the proposed 3 features at window size 3x3 are optimal in terms of recognition rate and PSNR compare to the ANN-based thresholding technique with different parameters presented in the literature. In biomedical imaging application, breast cancer continues to be a public health problem. In this thesis we presented a computer aided diagnosis (CAD) system for mass detection and classification in digitized mammograms, which performs mass detection on regions of interest (ROI) followed by the benign-malignant classification on detected masses. Three layers ANN with seven features is 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\u27s sensitivity 75%