Smart electronic gastroscope system using a cloud–edge collaborative framework

Liu, 2018, Comparative molecular analysis of gastrointestinal adenocarcinomas, Cancer Cell., 33, 721, 10.1016/j.ccell.2018.03.010 Torre, 2016, Global cancer incidence and mortality rates and trends–An update, Cancer Epidemiol. Biomark. Prev., 25, 16, 10.1158/1055-9965.EPI-15-0578 Chen, 2016, Cancer statistics in China, CA Cancer J. Clin., 66, 115, 10.3322/caac.21338 Jiang, 2017, Association of adjuvant chemotherapy with survival in patients with stage II or III gastric cancer, JAMA Surg., 152, 10.1001/jamasurg.2017.1087 Lagergren, 2017, Oesophageal cancer, Lancet, 390, 2383, 10.1016/S0140-6736(17)31462-9 Veitch, 2015, Optimizing early upper gastrointestinal cancer detection at endoscopy, Nat. Rev. Gastroenterol. Hepatol., 12, 660, 10.1038/nrgastro.2015.128 Menon, 2014, How commonly is upper gastrointestinal cancer missed at endoscopy? A meta-analysis, Endosc. Int. Open., 02, E46, 10.1055/s-0034-1365524 Y. Li, X. Li, X. Xie, L. Shen, Deep learning based gastric cancer identification, in: 2018 IEEE 15th Int. Symp. Biomed. Imaging (ISBI 2018), 2018, pp. 182–185, http://dx.doi.org/10.1109/ISBI.2018.8363550. Wang, 2018, Smart connected electronic gastroscope system for gastric cancer screening using multi-column convolutional neural networks, Int. J. Prod. Res., 7543, 1, 10.1080/00207543.2018.1540894 Kang, 2014, Cancer screening and prevention in China, Cancer Control., 131 Mohammed, 2018, A real time computer aided object detection of nasopharyngeal carcinoma using genetic algorithm and artificial neural network based on Haar feature fear, Futur. Gener. Comput. Syst., 89, 539, 10.1016/j.future.2018.07.022 Koh, 2019, Automated diagnosis of celiac disease using DWT and nonlinear features with video capsule endoscopy images, Futur. Gener. Comput. Syst., 90, 86, 10.1016/j.future.2018.07.044 Abdulhay, 2018, Gait and tremor investigation using machine learning techniques for the diagnosis of Parkinson disease, Futur. Gener. Comput. Syst., 83, 366, 10.1016/j.future.2018.02.009 Hadjileontiadis, 2016, Use of adaptive hybrid filtering process in Crohn’s disease lesion detection from real capsule endoscopy videos, Healthc. Technol. Lett., 3, 27, 10.1049/htl.2015.0055 Huang, 2015, Gastroesophageal reflux disease diagnosis using hierarchical heterogeneous descriptor fusion support vector machine, IEEE Trans. Biomed. Eng., PP, 1 Shi, 2016, Edge computing: Vision and challenges, IEEE Internet Things J., 3, 637, 10.1109/JIOT.2016.2579198 Liu, 2017, A new deep learning-based food recognition system for dietary assessment on an edge computing service infrastructure, IEEE Trans. Serv. Comput., 1374, 1, 10.1109/TSC.2016.2641658 Redmon, 2018 Krizhevsky, 2012, ImageNet classification with deep convolutional neural networks, Adv. Neural Inf. Process. Syst., 1 Szegedy, 2014, 1 K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: 2015 IEEE Conf. Comput. Vis. Pattern Recognit. 2016, pp. 1–9. Ning, 2018, Pattern classification for gastrointestinal stromal tumors by integration of radiomics and deep convolutional features, IEEE J. Biomed. Heal. Inform., 2194, 1 Chen, 2017, Automatic content understanding with cascaded spatial–temporal deep framework for capsule endoscopy videos, Neurocomputing, 229, 77, 10.1016/j.neucom.2016.06.077 Horie, 2018 Iakovidis, 2018, Detecting and locating gastrointestinal anomalies using deep learning and iterative cluster unification, IEEE Trans. Med. Imaging, 0062, 1 Hirasawa, 2018, Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images, Gastric Cancer, 21, 653, 10.1007/s10120-018-0793-2 Kanesaka, 2018, Computer-aided diagnosis for identifying and delineating early gastric cancers in magnifying narrow-band imaging, Gastrointest. Endosc., 87, 1339, 10.1016/j.gie.2017.11.029 der S.F. Van, 2016, Computer-aided detection of early neoplastic lesions in Barrett’s esophagus, Gastroenterology, 48, 617 Kenta Ishihara, Takahiro Ogawa, Miki Haseyama, Detection of gastric cancer risk from X-ray images via patch-based convolutional neural network, IEEE Int. Conf. Image Process, 2017, pp. 2055–2059. Liu, 2015, Global and local panoramic views for gastroscopy: An assisted method of gastroscopic lesion surveillance, IEEE Trans. Biomed. Eng., 62, 2296, 10.1109/TBME.2015.2424438 Adame, 2018, CUIDATS: An RFID–WSN hybrid monitoring system for smart health care environments, Futur. Gener. Comput. Syst., 78, 602, 10.1016/j.future.2016.12.023 Olaniyan, 2018, Opportunistic edge computing: Concepts, opportunities and research challenges, Futur. Gener. Comput. Syst., 89, 633, 10.1016/j.future.2018.07.040 Kobusińska, 2018, Emerging trends, issues and challenges in Internet of Things, Big Data and cloud computing, Futur. Gener. Comput. Syst., 87, 416, 10.1016/j.future.2018.05.021 Varghese, 2018, Next generation cloud computing: New trends and research directions, Futur. Gener. Comput. Syst., 79, 849, 10.1016/j.future.2017.09.020 Rahmani, 2018, Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things: A fog computing approach, Futur. Gener. Comput. Syst., 78, 641, 10.1016/j.future.2017.02.014 Din, 2018, Smart health monitoring and management system: Toward autonomous wearable sensing for internet of things using big data analytics, Futur. Gener. Comput. Syst. Zhuang, 2014, Efficient and robust large medical image retrieval in mobile cloud computing environment, Inf. Sci. (Ny)., 263, 60, 10.1016/j.ins.2013.10.013 Gu, 2017, Cost efficient resource management in fog computing supported medical cyber-physical system, IEEE Trans. Emerg. Top. Comput., 5, 108, 10.1109/TETC.2015.2508382 Wang, 2014, Video tonal stabilization via color states smoothing, IEEE Trans. Image Process., 23, 4838, 10.1109/TIP.2014.2358880 Z. Wang, A.C. Bovik, Multi-Scale Structural Similarity For Image Quality Assessment, in: Proc. IEEE Asilomar Conf. Signal, vol. 2, 2003, pp. 1398–1402, http://dx.doi.org/10.1109/ACSSC.2003.1292216. Yan, 2018, Low-dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss, IEEE Trans. Med. Imaging, 37, 1348, 10.1109/TMI.2018.2827462 A.G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, H. Adam, MobileNets: Efficient convolutional neural networks for mobile vision applications, in: IEEE Conf. Comput. Vis. Pattern Recognit, CVPR, 2017, arXiv:1704.04861. Hartigan, 1979, Algorithm AS 136: A K-Means clustering algorithm, J. R. Stat. Soc., 28, 100 Xie, 2018, A new CNN-based method for multi-directional car license plate detection, IEEE Trans. Intell. Transp. Syst., 19, 507, 10.1109/TITS.2017.2784093 Zahran, 2016, Modified extended tanh-function method and its applications to the Bogoyavlenskii equation, Appl. Math. Model., 40, 1769, 10.1016/j.apm.2015.08.018 Lin, 2017, Focal loss for dense object detection, IEEE Trans. Pattern Anal. Mach. Intell., PP, 2999 W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.Y. Fu, A.C. Berg, SSD: Single shot multibox detector, in: Eur. Conf. Comput. Vis., 2016, pp. 21–37. Tajbakhsh, 2016, Convolutional neural networks for medical image analysis: Full training or fine tuning?, IEEE Trans. Med. Imaging, 35, 1299, 10.1109/TMI.2016.2535302 Pan, 2010, A survey on transfer learning, IEEE Trans. Knowl. Data Eng., 22, 1345, 10.1109/TKDE.2009.191