{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T16:20:26Z","timestamp":1778516426187,"version":"3.51.4"},"reference-count":99,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,20]],"date-time":"2023-01-20T00:00:00Z","timestamp":1674172800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Given the increased interest in utilizing artificial intelligence as an assistive tool in the medical sector, colorectal polyp detection and classification using deep learning techniques has been an active area of research in recent years. The motivation for researching this topic is that physicians miss polyps from time to time due to fatigue and lack of experience carrying out the procedure. Unidentified polyps can cause further complications and ultimately lead to colorectal cancer (CRC), one of the leading causes of cancer mortality. Although various techniques have been presented recently, several key issues, such as the lack of enough training data, white light reflection, and blur affect the performance of such methods. This paper presents a survey on recently proposed methods for detecting polyps from colonoscopy. The survey covers benchmark dataset analysis, evaluation metrics, common challenges, standard methods of building polyp detectors and a review of the latest work in the literature. We conclude this paper by providing a precise analysis of the gaps and trends discovered in the reviewed literature for future work.<\/jats:p>","DOI":"10.3390\/s23031225","type":"journal-article","created":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T01:36:26Z","timestamp":1674437786000},"page":"1225","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Detection of Colorectal Polyps from Colonoscopy Using Machine Learning: A Survey on Modern Techniques"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7545-1605","authenticated-orcid":false,"given":"Khaled","family":"ELKarazle","sequence":"first","affiliation":[{"name":"School of Information and Communication Technologies, Swinburne University of Technology, Sarawak Campus, Kuching 93350, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9363-2319","authenticated-orcid":false,"given":"Valliappan","family":"Raman","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Data Science, Coimbatore Institute of Technology, Coimbatore 641014, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patrick","family":"Then","sequence":"additional","affiliation":[{"name":"School of Information and Communication Technologies, Swinburne University of Technology, Sarawak Campus, Kuching 93350, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Caslon","family":"Chua","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne 3122, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3053","DOI":"10.1111\/j.1572-0241.2000.03434.x","article-title":"Polyp guideline: Diagnosis, treatment, and surveillance for patients with colorectal polyps","volume":"95","author":"Bond","year":"2000","journal-title":"Am. J. Gastroenterol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"399","DOI":"10.5009\/gnl19097","article-title":"Risk factors for recurrent colorectal polyps","volume":"14","author":"Hao","year":"2020","journal-title":"Gut Liver"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/gastro\/got041","article-title":"Colorectal polyps and polyposis syndromes","volume":"2","author":"Shussman","year":"2014","journal-title":"Gastroenterol. Rep."},{"key":"ref_4","unstructured":"World Health Organization (2022, December 20). Colorectal Cancer. Available online: https:\/\/www.iarc.who.int\/cancer-type\/colorectal-cancer\/."},{"key":"ref_5","first-page":"4787","article-title":"Associations of sedentary lifestyle, obesity, smoking, alcohol use, and diabetes with the risk of colorectal cancer","volume":"57","author":"Wilkens","year":"1997","journal-title":"Cancer Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1426","DOI":"10.3346\/jkms.2016.31.9.1426","article-title":"Risk factors of advanced adenoma in small and diminutive colorectal polyp","volume":"31","author":"Jeong","year":"2016","journal-title":"J. Korean Med. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"990","DOI":"10.1136\/gut.14.12.990","article-title":"Progress report colonoscopy","volume":"14","author":"Williams","year":"1973","journal-title":"Gut"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"105031","DOI":"10.1016\/j.compbiomed.2021.105031","article-title":"An efficient real-time colonic polyp detection with YOLO algorithms trained by using negative samples and large datasets","volume":"141","author":"Pacal","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"103465","DOI":"10.1016\/j.bspc.2021.103465","article-title":"Automated colorectal polyp detection based on image enhancement and dual-path CNN architecture","volume":"73","author":"Nisha","year":"2022","journal-title":"Biomed. Signal. Process Control"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1016\/j.aej.2021.04.072","article-title":"Automatic polyp detection and segmentation using shuffle efficient channel attention network","volume":"61","author":"Yang","year":"2022","journal-title":"Alex. Eng. J."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Gong, E.J., Bang, C.S., Lee, J.J., Seo, S.I., Yang, Y.J., Baik, G.H., and Kim, J.W. (2022). No-Code Platform-Based Deep-Learning Models for Prediction of Colorectal Polyp Histology from White-Light Endoscopy Images: Development and Performance Verification. J. Pers. Med., 12.","DOI":"10.3390\/jpm12060963"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"102625","DOI":"10.1016\/j.media.2022.102625","article-title":"Polyp detection on video colonoscopy using a hybrid 2D\/3D CNN","volume":"82","author":"Puyal","year":"2022","journal-title":"Med. Image Anal."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"105760","DOI":"10.1016\/j.compbiomed.2022.105760","article-title":"Colorectal polyp region extraction using saliency detection network with neutrosophic enhancement","volume":"147","author":"Hu","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_14","unstructured":"Mohammed, A.K., Yildirim-Yayilgan, S., Farup, I., Pedersen, M., and Hovde, O. (2018, January 3\u20136). Y-Net: A deep Convolutional Neural Network to Polyp Detection. Proceedings of the British Machine Vision Conference 2018, BMVC 2018, Tyne, UK."},{"key":"ref_15","first-page":"108","article-title":"Medical Imaging: Computer-Aided Diagnosis\u2014Deep ensemble learning of virtual endoluminal views for polyp detection in CT colonography","volume":"10134","author":"Umehara","year":"2017","journal-title":"SPIE Proc."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"10375","DOI":"10.1007\/s00521-021-06496-4","article-title":"Real-time polyp detection model using convolutional neural networks","volume":"34","author":"Herrero","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_17","unstructured":"Jha, D., Smedsrud, P.H., Riegler, M.A., Halvorsen, P., Lange, T.D., Johansen, D., and Johansen, H.D. (2020). International Conference on Multimedia Modeling, Springer."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1007\/s11548-013-0926-3","article-title":"Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer","volume":"9","author":"Silva","year":"2014","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1049\/iet-cvi.2019.0300","article-title":"Polyp detection using CNNs in colonoscopy video","volume":"14","author":"Mohammadi","year":"2020","journal-title":"IET Comput. Vis."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2029","DOI":"10.1109\/JBHI.2021.3049304","article-title":"A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation","volume":"25","author":"Jha","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.compmedimag.2015.02.007","article-title":"WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians","volume":"43","author":"Bernal","year":"2015","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_22","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2022, December 21). U-Net: Convolutional Networks for Biomedical Image Segmentation. Available online: http:\/\/lmb.informatik.uni-freiburg.de\/."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3166","DOI":"10.1016\/j.patcog.2012.03.002","article-title":"Towards automatic polyp detection with a polyp appearance model","volume":"45","author":"Bernal","year":"2012","journal-title":"Pattern Recognit."},{"key":"ref_24","first-page":"4037190","article-title":"A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images","volume":"2017","author":"Bernal","year":"2017","journal-title":"J. Healthc. Eng."},{"key":"ref_25","unstructured":"(2022, December 21). International Conference on Pattern Recognition, EndoTect 2020. Available online: https:\/\/endotect.com\/."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"43669","DOI":"10.1109\/ACCESS.2022.3168693","article-title":"BlazeNeo: Blazing Fast Polyp Segmentation and Neoplasm Detection","volume":"10","author":"An","year":"2022","journal-title":"IEEE Access."},{"key":"ref_27","unstructured":"Lan, P.N., An, N.S., Hang, D.V., Van Long, D., Trung, T.Q., Thuy, N.T., and Sang, D.V. (2021). Advances in Visual Computing, Springer. ISVC 2021. Lecture Notes in Computer Science."},{"key":"ref_28","unstructured":"Ali, S., Jha, D., Ghatwary, N., Realdon, S., Cannizzaro, R., Salem, O.E., Lamarque, D., Daul, C., Riegler, M.A., and Anonsen, K.V. (2021). PolypGen: A multi-center polyp detection and segmentation dataset for generalisability assessment. arXiv."},{"key":"ref_29","unstructured":"Ali, S., Ghatwary, N., Jha, D., Isik-Polat, E., Polat, G., Yang, C., Li, W., Galdran, A., Ballester, M.-\u00c1.G., and Thambawita, V. (2022). East, Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"102002","DOI":"10.1016\/j.media.2021.102002","article-title":"Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy","volume":"70","author":"SAli","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"960","DOI":"10.1016\/j.gie.2020.07.060","article-title":"Development of a computer-aided detection system for colonoscopy and a publicly accessible large colonoscopy video database (with video)","volume":"93","author":"Misawa","year":"2021","journal-title":"Gastrointest. Endosc."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1397","DOI":"10.1080\/00365521.2022.2085059","article-title":"A video based benchmark data set (ENDOTEST) to evaluate computer-aided polyp detection systems","volume":"57","author":"Fitting","year":"2022","journal-title":"Scand. J. Gastroenterol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1109\/TMI.2015.2487997","article-title":"Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information","volume":"35","author":"Tajbakhsh","year":"2016","journal-title":"IEEE Trans. Med. Imaging."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2051","DOI":"10.1109\/TMI.2016.2547947","article-title":"Computer-Aided Classification of Gastrointestinal Lesions in Regular Colonoscopy","volume":"35","author":"Mesejo","year":"2016","journal-title":"IEEE Trans. Med. Imaging."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Cardoso, M.J., Arbel, T., Luo, X., Wesarg, S., Reichl, T., Gonz\u00e1lez Ballester, M.\u00c1., McLeod, J., Drechsler, K., Peters, T., and Erdt, M. (2017). Computer Assisted and Robotic Endoscopy and Clinical Image-Based Procedures, Springer International Publishing.","DOI":"10.1007\/978-3-319-67543-5"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s13735-017-0141-z","article-title":"A review of semantic segmentation using deep neural networks","volume":"7","author":"Guo","year":"2018","journal-title":"Int. J. Multimed. Inf. Retr."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Safarov, S., and Whangbo, T. (2021). A-DenseUNet: Adaptive Densely Connected UNet for Polyp Segmentation in Colonoscopy Images with Atrous Convolution. Sensors, 21.","DOI":"10.21203\/rs.3.rs-158417\/v1"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Sun, X., Zhang, P., Wang, D., Cao, Y., and Liu, B. (2019, January 16\u201319). Colorectal polyp segmentation by U-Net with dilation convolution. Proceedings of the 18th IEEE International Conference on Machine Learning and Applications. ICMLA, Boca Raton, FL, USA.","DOI":"10.1109\/ICMLA.2019.00148"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal Mach. Intell."},{"key":"ref_40","unstructured":"Simonyan, K., and Zisserman, A. (2022, December 22). Very Deep Convolutional Networks for Large-Scale Image Recognition. Available online: http:\/\/www.robots.ox.ac.uk\/."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1741","DOI":"10.1002\/ima.22568","article-title":"An improved framework for polyp image segmentation based on SegNet architecture","volume":"31","author":"Afify","year":"2021","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully Convolutional Networks for Semantic Segmentation","volume":"39","author":"Shelhamer","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1109\/JBHI.2016.2637004","article-title":"Integrating Online and Offline Three-Dimensional Deep Learning for Automated Polyp Detection in Colonoscopy Videos","volume":"21","author":"Yu","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_44","first-page":"101","article-title":"Fully convolutional neural networks for polyp segmentation in colonoscopy","volume":"10134","author":"Brandao","year":"2017","journal-title":"Med. Imaging 2017 Comput.-Aided Diagn."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_46","first-page":"538","article-title":"Localization of Polyps in WCE Images Using Deep Learning Segmentation Methods: A Comparative Study","volume":"1567","author":"Jain","year":"2022","journal-title":"Commun. Comput. Inf. Sci. CCIS"},{"key":"ref_47","first-page":"586","article-title":"Comparative study of object detection algorithms","volume":"4","author":"Yadav","year":"2017","journal-title":"Int. Res. J. Eng. Technol."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Tashk, A., and Nadimi, E. (2020, January 19\u201324). An Innovative Polyp Detection Method from Colon Capsule Endoscopy Images Based on A Novel Combination of RCNN and DRLSE. Proceedings of the 2020 IEEE Congress on Evolutionary Computation (CEC), Glasgow, UK.","DOI":"10.1109\/CEC48606.2020.9185629"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Qadir, H.A., Shin, Y., Solhusvik, J., Bergsland, J., Aabakken, L., and Balasingham, I. (2019, January 8\u201310). Polyp Detection and Segmentation using Mask R-CNN: Does a Deeper Feature Extractor CNN Always Perform Better?. Proceedings of the 2019 13th International Symposium on Medical Information and Communication Technology (ISMICT), Oslo, Norway.","DOI":"10.1109\/ISMICT.2019.8743694"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"103019","DOI":"10.1016\/j.bspc.2021.103019","article-title":"A self-attention based faster R-CNN for polyp detection from colonoscopy images","volume":"70","author":"Chen","year":"2021","journal-title":"Biomed. Signal. Process Control."},{"key":"ref_52","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2022, December 23). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Available online: https:\/\/github.com\/."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"11374","DOI":"10.1109\/JSEN.2020.3036005","article-title":"A new approach to polyp detection by pre-processing of images and enhanced faster r-cnn","volume":"21","author":"Qian","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"106531","DOI":"10.1016\/j.compeleceng.2019.106531","article-title":"Application of deep learning for autonomous detection and localization of colorectal polyps in wireless colon capsule endoscopy","volume":"81","author":"Nadimi","year":"2020","journal-title":"Comput. Electr. Eng."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1049\/cje.2019.03.005","article-title":"Computer-assisted detection of colonic polyps using improved faster R-CNN","volume":"28","author":"Li","year":"2019","journal-title":"Chin. J. Electron."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Leibe, B., Matas, J., Sebe, N., and Welling, M. (2016). Computer Vision\u2014ECCV 2016, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-319-46454-1"},{"key":"ref_57","first-page":"45058","article-title":"Colonic Polyp Detection in Endoscopic Videos with Single Shot Detection Based Deep Convolutional Neural Network","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Access."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2805607","DOI":"10.1155\/2022\/2805607","article-title":"Detection and Classification of Colorectal Polyp Using Deep Learning","volume":"2022","author":"Tanwar","year":"2022","journal-title":"Biomed. Res. Int."},{"key":"ref_59","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2022, December 23). You Only Look Once: Unified, Real-Time Object Detection. Available online: http:\/\/pjreddie.com\/yolo\/."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1515\/cdbme-2022-1071","article-title":"YOLO networks for polyp detection: A human-in-the-loop training approach","volume":"8","author":"Eixelberger","year":"2022","journal-title":"Curr. Dir. Biomed. Eng."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Doniyorjon, M., Madinakhon, R., Shakhnoza, M., and Cho, Y.-I. (2022). An Improved Method of Polyp Detection Using Custom YOLOv4-Tiny. Appl. Sci., 12.","DOI":"10.3390\/app122110856"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.patcog.2018.05.026","article-title":"Polyp detection during colonoscopy using a regression-based convolutional neural network with a tracker","volume":"83","author":"Zhang","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Reddy, J.S.C., Venkatesh, C., Sinha, S., and Mazumdar, S. (2022, January 6\u20137). Real time Automatic Polyp Detection in White light Endoscopy videos using a combination of YOLO and DeepSORT. Proceedings of the PCEMS 2022\u20141st International Conference on the Paradigm Shifts in Communication, Embedded Systems. Machine Learning and Signal Processing, Nagpur, India.","DOI":"10.1109\/PCEMS55161.2022.9807988"},{"key":"ref_64","first-page":"2485934","article-title":"Multiclassification of Endoscopic Colonoscopy Images Based on Deep Transfer Learning","volume":"2021","author":"Wang","year":"2021","journal-title":"Comput. Math Methods Med."},{"key":"ref_65","unstructured":"NVenkatayogi, N., Kara, O.C., Bonyun, J., Ikoma, N., and Alambeigi, F. (November, January 30). Classification of Colorectal Cancer Polyps via Transfer Learning and Vision-Based Tactile Sensing. Proceedings of the IEEE Sensors, Dallas, TX, USA."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"6584725","DOI":"10.1155\/2016\/6584725","article-title":"Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification","volume":"2016","author":"Ribeiro","year":"2016","journal-title":"Comput. Math Methods Med."},{"key":"ref_67","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very deep convolutional networks for large-scale image recognition. Proceedings of the 3rd International Conference on Learning Representations (ICLR 2015), San Diego, CA, USA. Available online: http:\/\/www.robots.ox.ac.uk\/."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Sun, X., Wang, D., Zhang, C., Zhang, P., Xiong, Z., Cao, Y., Liu, B., Liu, X., and Chen, S. (2020, January 9\u201311). Colorectal Polyp Detection in Real-world Scenario: Design and Experiment Study. Proceedings of the International Conference on Tools with Artificial Intelligence, ICTAI, Baltimore, MD, USA.","DOI":"10.1109\/ICTAI50040.2020.00113"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"2507","DOI":"10.1016\/j.procs.2020.09.325","article-title":"Colorectal Polyp Classification Based On Latent Sharing Features Domain from Multiple Endoscopy Images","volume":"176","author":"Usami","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"4347","DOI":"10.1038\/s41598-021-83788-8","article-title":"Computational learning of features for automated colonic polyp classification","volume":"11","author":"Bora","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Tashk, A., Herp, J., Nadimi, E., and Sahin, K.E. (2022, January 5\u20137). A CNN Architecture for Detection and Segmentation of Colorectal Polyps from CCE Images. Proceedings of the 2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS), Genova, Italy. Accepted\/In press.","DOI":"10.1109\/IPAS55744.2022.10052795"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"26440","DOI":"10.1109\/ACCESS.2019.2900672","article-title":"Ensemble of Instance Segmentation Models for Polyp Segmentation in Colonoscopy Images","volume":"7","author":"Kang","year":"2019","journal-title":"IEEE Access."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"106114","DOI":"10.1016\/j.cmpb.2021.106114","article-title":"Automatic colonic polyp detection using integration of modified deep residual convolutional neural network and ensemble learning approaches","volume":"206","author":"Liew","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep Learning with Depthwise Separable Convolutions. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Jinsakul, N., Tsai, C.-F., Tsai, C.-E., and Wu, P. (2019). Enhancement of Deep Learning in Image Classification Performance Using Xception with the Swish Activation Function for Colorectal Polyp Preliminary Screening. Mathematics, 7.","DOI":"10.3390\/math7121170"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"2410","DOI":"10.1007\/s10489-022-03689-9","article-title":"A deep ensemble learning method for colorectal polyp classification with optimized network parameters","volume":"53","author":"Younas","year":"2022","journal-title":"Appl. Intell."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Liew, W.S., Tang, T.B., and Lu, C.-K. (2022, January 17\u201318). Computer-aided diagnostic tool for classification of colonic polyp assessment. Proceedings of the International Conference on Artificial Intelligence for Smart Community, Seri Iskandar, Malaysia.","DOI":"10.1007\/978-981-16-2183-3_71"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Lo, C.-M., Yeh, Y.-H., Tang, J.-H., Chang, C.-C., and Yeh, H.-J. (2022). Rapid Polyp Classification in Colonoscopy Using Textural and Convolutional Features. Healthcare, 10.","DOI":"10.3390\/healthcare10081494"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"844391","DOI":"10.3389\/fgene.2022.844391","article-title":"An Ensemble-Based Deep Convolutional Neural Network for Computer-Aided Polyps Identification from Colonoscopy","volume":"13","author":"Sharma","year":"2022","journal-title":"Front. Genet."},{"key":"ref_84","unstructured":"Rani, N., Verma, R., and Jinda, A. (2022). Handbook of Intelligent Computing and Optimization for Sustainable Development, Scrivener Publishing LLC."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"17678","DOI":"10.1038\/s41598-022-21574-w","article-title":"A stacking-based artificial intelligence framework for an effective detection and localization of colon polyps","volume":"12","author":"Albuquerque","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Liao, H.-Y.M., Wu, Y.-H., Chen, P.-Y., Hsieh, J.-W., and Yeh, I.-H. (2020, January 14\u201319). CSPNet: A new backbone that can enhance learning capability of CNN. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"ref_87","first-page":"616","article-title":"SwinE-Net: Hybrid deep learning approach to novel polyp segmentation using convolutional neural network and Swin Transformer","volume":"9","author":"Park","year":"2022","journal-title":"J. Comput. Des. Eng."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2022, December 24). Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. Available online: https:\/\/github.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_89","unstructured":"Tan, M., and Le, Q.V. (2019, January 9\u201315). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the 36th International Conference on Machine Learning, ICML, Long Beach, CA, USA."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1038\/s41598-022-06264-x","article-title":"A promising deep learning-assistive algorithm for histopathological screening of colorectal cancer","volume":"12","author":"Ho","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"4749","DOI":"10.1007\/s00330-021-08532-2","article-title":"Deep learning in CT colonography: Differentiating premalignant from benign colorectal polyps","volume":"32","author":"Wesp","year":"2022","journal-title":"Eur. Radiol."},{"key":"ref_92","first-page":"1","article-title":"A novel AI device for real-time optical characterization of colorectal polyps","volume":"5","author":"Biffi","year":"2022","journal-title":"NPJ Digit. Med."},{"key":"ref_93","unstructured":"(2022, December 24). GI GeniusTM Intelligent Endoscopy Module | Medtronic. Available online: https:\/\/www.medtronic.com\/covidien\/en-us\/products\/gastrointestinal-artificial-intelligence\/gi-genius-intelligent-endoscopy.html."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Ellahyani, A., El Jaafari, I., Charfi, S., and El Ansari, M. (2022). Fine-tuned deep neural networks for polyp detection in colonoscopy images. Pers. Ubiquitous Comput.","DOI":"10.1007\/s00779-021-01660-y"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1148\/radiol.2021202363","article-title":"Machine learning-based differentiation of benign and premalignant colorectal polyps detected with CT colonography in an asymptomatic screening population: A proof-of-concept study","volume":"299","author":"Grosu","year":"2021","journal-title":"Radiology"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Ma, C., Jiang, H., Ma, L., and Chang, Y. (2022). A Real-Time Polyp Detection Framework for Colonoscopy Video, Springer International Publishing.","DOI":"10.1007\/978-3-031-18907-4_21"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Eu, C.Y., Tang, T.B., and Lu, C.-K. (2022). Automatic Polyp Segmentation in Colonoscopy Images Using Single Network Model: SegNet, Springer Nature.","DOI":"10.1007\/978-981-16-2183-3_69"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Carrinho, P., and Falcao, G. (2022). Highly accurate and fast YOLOv4-Based polyp detection. SSRN Electron. J.","DOI":"10.2139\/ssrn.4227573"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"102363","DOI":"10.1016\/j.artmed.2022.102363","article-title":"An end-to-end tracking method for polyp detectors in colonoscopy videos","volume":"131","author":"Yu","year":"2022","journal-title":"Artif. Intell. 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