Selected Papers from the First International Symposium on Future ICT (Future-ICT 2019) in Conjunction with 4th International Symposium on Mobile Internet Security (MobiSec 2019)

The International Symposium on Future ICT (Future-ICT 2019) in conjunction with the 4th International Symposium on Mobile Internet Security (MobiSec 2019) was held on 17–19 October 2019 in Taichung, Taiwan. The symposium provided academic and industry professionals an opportunity to discuss the latest issues and progress in advancing smart applications based on future ICT and its relative security. The symposium aimed to publish high-quality papers strictly related to the various theories and practical applications concerning advanced smart applications, future ICT, and related communications and networks. It was expected that the symposium and its publications would be a trigger for further related research and technology improvements in this field

Energy Efficient Security Framework for Wireless Local Area Networks

Wireless networks are susceptible to network attacks due to their inherentvulnerabilities. The radio signal used in wireless transmission canarbitrarily propagate through walls and windows; thus a wireless networkperimeter is not exactly known. This leads them to be more vulnerable toattacks such as eavesdropping, message interception and modifications comparedto wired-line networks. Security services have been used as countermeasures toprevent such attacks, but they are used at the expense of resources that arescarce especially, where wireless devices have a very limited power budget.Hence, there is a need to provide security services that are energy efficient.In this dissertation, we propose an energy efficient security framework. Theframework aims at providing security services that take into account energyconsumption. We suggest three approaches to reduce the energy consumption ofsecurity protocols: replacement of standard security protocol primitives thatconsume high energy while maintaining the same security level, modification ofstandard security protocols appropriately, and a totally new design ofsecurity protocol where energy efficiency is the main focus. From ourobservation and study, we hypothesize that a higher level of energy savings isachievable if security services are provided in an adjustable manner. Wepropose an example tunable security or TuneSec system, which allows areasonably fine-grained security tuning to provide security services at thewireless link level in an adjustable manner.We apply the framework to several standard security protocols in wirelesslocal area networks and also evaluate their energy consumption performance.The first and second methods show improvements of up to 70% and 57% inenergy consumption compared to plain standard security protocols,respectively. The standard protocols can only offer fixed-level securityservices, and the methods applied do not change the security level. The thirdmethod shows further improvement compared to fixed-level security by reducing(about 6% to 40%) the energy consumed. This amount of energy saving can bevaried depending on the configuration and security requirements

Smart Wireless Sensor Networks

The recent development of communication and sensor technology results in the growth of a new attractive and challenging area - wireless sensor networks (WSNs). A wireless sensor network which consists of a large number of sensor nodes is deployed in environmental fields to serve various applications. Facilitated with the ability of wireless communication and intelligent computation, these nodes become smart sensors which do not only perceive ambient physical parameters but also be able to process information, cooperate with each other and self-organize into the network. These new features assist the sensor nodes as well as the network to operate more efficiently in terms of both data acquisition and energy consumption. Special purposes of the applications require design and operation of WSNs different from conventional networks such as the internet. The network design must take into account of the objectives of specific applications. The nature of deployed environment must be considered. The limited of sensor nodes� resources such as memory, computational ability, communication bandwidth and energy source are the challenges in network design. A smart wireless sensor network must be able to deal with these constraints as well as to guarantee the connectivity, coverage, reliability and security of network's operation for a maximized lifetime. This book discusses various aspects of designing such smart wireless sensor networks. Main topics includes: design methodologies, network protocols and algorithms, quality of service management, coverage optimization, time synchronization and security techniques for sensor networks

Security protocols suite for machine-to-machine systems

Nowadays, the great diffusion of advanced devices, such as smart-phones, has shown that there is a growing trend to rely on new technologies to generate and/or support progress; the society is clearly ready to trust on next-generation communication systems to face today’s concerns on economic and social fields. The reason for this sociological change is represented by the fact that the technologies have been open to all users, even if the latter do not necessarily have a specific knowledge in this field, and therefore the introduction of new user-friendly applications has now appeared as a business opportunity and a key factor to increase the general cohesion among all citizens. Within the actors of this technological evolution, wireless machine-to-machine (M2M) networks are becoming of great importance. These wireless networks are made up of interconnected low-power devices that are able to provide a great variety of services with little or even no user intervention. Examples of these services can be fleet management, fire detection, utilities consumption (water and energy distribution, etc.) or patients monitoring. However, since any arising technology goes together with its security threats, which have to be faced, further studies are necessary to secure wireless M2M technology. In this context, main threats are those related to attacks to the services availability and to the privacy of both the subscribers’ and the services providers’ data. Taking into account the often limited resources of the M2M devices at the hardware level, ensuring the availability and privacy requirements in the range of M2M applications while minimizing the waste of valuable resources is even more challenging. Based on the above facts, this Ph. D. thesis is aimed at providing efficient security solutions for wireless M2M networks that effectively reduce energy consumption of the network while not affecting the overall security services of the system. With this goal, we first propose a coherent taxonomy of M2M network that allows us to identify which security topics deserve special attention and which entities or specific services are particularly threatened. Second, we define an efficient, secure-data aggregation scheme that is able to increase the network lifetime by optimizing the energy consumption of the devices. Third, we propose a novel physical authenticator or frame checker that minimizes the communication costs in wireless channels and that successfully faces exhaustion attacks. Fourth, we study specific aspects of typical key management schemes to provide a novel protocol which ensures the distribution of secret keys for all the cryptographic methods used in this system. Fifth, we describe the collaboration with the WAVE2M community in order to define a proper frame format actually able to support the necessary security services, including the ones that we have already proposed; WAVE2M was funded to promote the global use of an emerging wireless communication technology for ultra-low and long-range services. And finally sixth, we provide with an accurate analysis of privacy solutions that actually fit M2M-networks services’ requirements. All the analyses along this thesis are corroborated by simulations that confirm significant improvements in terms of efficiency while supporting the necessary security requirements for M2M networks

Enabling Secure Direct Connectivity Under Intermittent Cellular Network Assistance

This work targets at investigating direct communications as a promising technology for the next-generation 5G wireless ecosystem that improves the degrees of spatial reuse and creates new opportunities for users in proximity. While direct connectivity has originally emerged as a technology enabler for public safety services, it is likely to remain in the heart of the 5G ecosystem by spawning a wide diversity of proximate applications and services. Direct communications couples together the centralized and the distributed network architectures, and as such requires respective enablers for secure, private, and trusted data exchange especially when cellular control link is not available at all times. Within the research group, the author was tasked to provide the state-of-the-art technology overview and to propose a novel algorithm for maintaining security functions of proximate devices in case of unreliable cellular connectivity, whenever a new device joins the secure group of users or an existing device leaves it. The proposed solution and its rigorous practical implementation detailed in this work open door to a new generation of secure proximity-based services and applications in future wireless communications systems

A full privacy-preserving distributed batch-based certificate-less aggregate signature authentication scheme for healthcare wearable wireless medical sensor networks (HWMSNs)

The dynamic connectivity and functionality of sensors has revolutionized remote monitoring applications thanks to the combination of IoT and wireless sensor networks (WSNs). Wearable wireless medical sensor nodes allow continuous monitoring by amassing physiological data, which is very useful in healthcare applications. These text data are then sent to doctors via IoT devices so they can make an accurate diagnosis as soon as possible. However, the transmission of medical text data is extremely vulnerable to security and privacy assaults due to the open nature of the underlying communication medium. Therefore, a certificate-less aggregation-based signature system has been proposed as a solution to the issue by using elliptic curve public key cryptography (ECC) which allows for a highly effective technique. The cost of computing has been reduced by 93% due to the incorporation of aggregation technology. The communication cost is 400 bits which is a significant reduction when compared with its counterparts. The results of the security analysis show that the scheme is robust against forging, tampering, and man-in-the-middle attacks. The primary innovation is that the time required for signature verification can be reduced by using point addition and aggregation. In addition, it does away with the reliance on a centralized medical server in order to do verification. By taking a distributed approach, it is able to fully preserve user privacy, proving its superiority

A Low-Energy Security Solution for IoT-Based Smart Farms

This work proposes a novel configuration of the Transport Layer Security protocol (TLS), suitable for low energy Internet of Things (IoT), applications. The motivation behind the redesign of TLS is energy consumption minimisation and sustainable farming, as exemplified by an application domain of aquaponic smart farms. The work therefore considers decentralisation of a formerly centralised security model, with a focus on reducing energy consumption for battery powered devices. The research presents a four-part investigation into the security solution, composed of a risk assessment, energy analysis of authentication and data exchange functions, and finally the design and verification of a novel consensus authorisation mechanism. The first investigation considered traditional risk-driven threat assessment, but to include energy reduction, working towards device longevity within a content-oriented framework. Since the aquaponics environments include limited but specific data exchanges, a content-oriented approach produced valuable insights into security and privacy requirements that would later be tested by implementing a variety of mechanisms available on the ESP32. The second and third investigations featured the energy analysis of authentication and data exchange functions respectively, where the results of the risk assessment were implemented to compare the re-configurations of TLS mechanisms and domain content. Results concluded that selective confidentiality and persistent secure sessions between paired devices enabled considerable improvements for energy consumptions, and were a good reflection of the possibilities suggested by the risk assessment. The fourth and final investigation proposed a granular authorisation design to increase the safety of access control that would otherwise be binary in TLS. The motivation was for damage mitigation from inside attacks or network faults. The approach involved an automated, hierarchy-based, decentralised network topology to reduce data duplication whilst still providing robustness beyond the vulnerability of central governance. Formal verification using model-checking indicated a safe design model, using four automated back-ends. The research concludes that lower energy IoT solutions for the smart farm application domain are possible

Proceedings of the 5th International Workshop on Reconfigurable Communication-centric Systems on Chip 2010 - ReCoSoC\u2710 - May 17-19, 2010 Karlsruhe, Germany. (KIT Scientific Reports ; 7551)

ReCoSoC is intended to be a periodic annual meeting to expose and discuss gathered expertise as well as state of the art research around SoC related topics through plenary invited papers and posters. The workshop aims to provide a prospective view of tomorrow\u27s challenges in the multibillion transistor era, taking into account the emerging techniques and architectures exploring the synergy between flexible on-chip communication and system reconfigurability

Location Privacy in VANETs: Improved Chaff-Based CMIX and Privacy-Preserving End-to-End Communication

VANETs communication systems are technologies and defined policies that can be formed to enable ITS applications to provide road traffic efficacy, warning about such issues as environmental dangers, journey circumstances, and in the provision of infotainment that considerably enhance transportation safety and quality. The entities in VANETs, generally vehicles, form part of a massive network known as the Internet of Vehicles (IoV). The deployment of large-scale VANETs systems is impossible without ensuring that such systems are themselves are safe and secure, protecting the privacy of their users. There is a risk that cars might be hacked, or their sensors become defective, causing inaccurate information to be sent across the network. Consequently, the activities and credentials of participating vehicles should be held responsible and quickly broadcast throughout a vast VANETs, considering the accountability in the system. The openness of wireless communication means that an observer can eavesdrop on vehicular communication and gain access or otherwise deduce users' sensitive information, and perhaps profile vehicles based on numerous factors such as tracing their travels and the identification of their home/work locations. In order to protect the system from malicious or compromised entities, as well as to preserve user privacy, the goal is to achieve communication security, i.e., keep users' identities hidden from both the outside world and the security infrastructure and service providers. Being held accountable while still maintaining one's privacy is a difficult balancing act. This thesis explores novel solution paths to the above challenges by investigating the impact of low-density messaging to improve the security of vehicle communications and accomplish unlinkability in VANETs. This is achieved by proposing an improved chaff-based CMIX protocol that uses fake messages to increase density to mitigate tracking in this scenario. Recently, Christian \etall \cite{vaas2018nowhere} proposed a Chaff-based CMIX scheme that sends fake messages under the presumption low-density conditions to enhance vehicle privacy and confuse attackers. To accomplish full unlinkability, we first show the following security and privacy vulnerabilities in the Christian \etall scheme: linkability attacks outside the CMIX may occur due to deterministic data-sharing during the authentication phase (e.g., duplicate certificates for each communication). Adversaries may inject fake certificates, which breaks Cuckoo Filters' (CFs) updates authenticity, and the injection may be deniable. CMIX symmetric key leakage outside the coverage may occur. We propose a VPKI-based protocol to mitigate these issues. First, we use a modified version of Wang \etall's \cite{wang2019practical} scheme to provide mutual authentication without revealing the real identity. To this end, a vehicle's messages are signed with a different pseudo-identity “certificate”. Furthermore, the density is increased via the sending of fake messages during low traffic periods to provide unlinkability outside the mix-zone. Second, unlike Christian \etall's scheme, we use the Adaptive Cuckoo Filter (ACF) instead of CF to overcome the effects of false positives on the whole filter. Moreover, to prevent any alteration of the ACFs, only RUSs distribute the updates, and they sign the new fingerprints. Third, mutual authentication prevents any leakage from the mix zones' symmetric keys by generating a fresh one for each communication through a Diffie–Hellman key exchange. As a second main contribution of this thesis, we focus on the V2V communication without the interference of a Trusted Third Party (TTP)s in case this has been corrupted, destroyed, or is out of range. This thesis presents a new and efficient end-to-end anonymous key exchange protocol based on Yang \etall's \cite{yang2015self} self-blindable signatures. In our protocol, vehicles first privately blind their own private certificates for each communication outside the mix-zone and then compute an anonymous shared key based on zero-knowledge proof of knowledge (PoK). The efficiency comes from the fact that once the signatures are verified, the ephemeral values in the PoK are also used to compute a shared key through an authenticated Diffie-Hellman key exchange protocol. Therefore, the protocol does not require any further external information to generate a shared key. Our protocol also does not require interfacing with the Roadside Units or Certificate Authorities, and hence can be securely run outside the mixed-zones. We demonstrate the security of our protocol in ideal/real simulation paradigms. Hence, our protocol achieves secure authentication, forward unlinkability, and accountability. Furthermore, the performance analysis shows that our protocol is more efficient in terms of computational and communications overheads compared to existing schemes.Kuwait Cultural Offic

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