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Current Medical Imaging

Author(s): Javeria Naz*, Muhammad Sharif, Mussarat Yasmin*, Mudassar Raza and Muhammad Attique Khan

DOI: 10.2174/1573405616666200928144626

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Detection and Classification of Gastrointestinal Diseases using Machine Learning

Page: [479 - 490] Pages: 12

  • * (Excluding Mailing and Handling)

Abstract

Background: Traditional endoscopy is an invasive and painful method of examining the gastrointestinal tract (GIT) not supported by physicians and patients. To handle this issue, video endoscopy (VE) or wireless capsule endoscopy (WCE) is recommended and utilized for GIT examination. Furthermore, manual assessment of captured images is not possible for an expert physician because it’s a time taking task to analyze thousands of images thoroughly. Hence, there comes the need for a Computer-Aided-Diagnosis (CAD) method to help doctors analyze images. Many researchers have proposed techniques for automated recognition and classification of abnormality in captured images.

Methods: In this article, existing methods for automated classification, segmentation and detection of several GI diseases are discussed. Paper gives a comprehensive detail about these state-of-theart methods. Furthermore, literature is divided into several subsections based on preprocessing techniques, segmentation techniques, handcrafted features based techniques and deep learning based techniques. Finally, issues, challenges and limitations are also undertaken.

Results: A comparative analysis of different approaches for the detection and classification of GI infections.

Conclusion: This comprehensive review article combines information related to a number of GI diseases diagnosis methods at one place. This article will facilitate the researchers to develop new algorithms and approaches for early detection of GI diseases detection with more promising results as compared to the existing ones of literature.

Keywords: Gastrointestinal Tract (GIT), Convolutional Neural Network (CNN), Wireless Capsule Endoscopy (WCE), machine learning, Computer Aided Design (CAD), handcrafted features.

Graphical Abstract

[1]
International Agency of Research on Cancer WHO: Colorectal cancer 2018.http://gco.iarc.fr/today/data/factsheets/cancers/ 10_8_9-Colorectum-fact-sheet.pdf [Accessed Date: 20-05-2019]
[2]
Park SR, Kim MJ, Ryu KW, et al. Prognostic value of preoperative clinical staging assessed by computed tomography in resectable gastric cancer patients: a viewpoint in the era of preoperative treatment. Ann Surg 2010; 251(3): 428-35.
[http://dx.doi.org/10.1097/SLA.0b013e3181ca69a7] [PMID: 20179530]
[3]
Statista. Prevalence of diagnosed gastrointestinal conditions in selected countries as of 2018. https://www.statista.com/statistics/418515/adult-prevalence-of-gastrointestinal-conditions-by-country/ . [Accessed Date: 03-06-2019]
[4]
Master N. Diseases of the digestive system deaths per 100,000 population (1995-1998). https://www.nationmaster.com/country-info/stats/Health/Digestive-disease-deaths [Accessed Date: 03-06-2019]
[5]
Kim Y-W, Baik YH, Yun YH, et al. Improved quality of life outcomes after laparoscopy-assisted distal gastrectomy for early gastric cancer: results of a prospective randomized clinical trial. Ann Surg 2008; 248(5): 721-7.
[http://dx.doi.org/10.1097/SLA.0b013e318185e62e] [PMID: 18948798]
[6]
Asperti A, Mastronardo C. The effectiveness of data augmentation for detection of gastrointestinal diseases from endoscopical images. arXiv preprint arXiv:171203689 2017.
[7]
Valdastri P, Simi M, Webster RJ III. Advanced technologies for gastrointestinal endoscopy. Annu Rev Biomed Eng 2012; 14: 397-429.
[http://dx.doi.org/10.1146/annurev-bioeng-071811-150006] [PMID: 22655598]
[8]
Young PV. 2019. https://www.pinterest.com/pin/ 237142 736608256441/?autologin=true&lp=true . [Accessed Date: 15-08-2019]
[9]
Tannapfel A, Schmelzer S, Benicke M, et al. Expression of the p53 homologues p63 and p73 in multiple simultaneous gastric cancer. J Pathol 2001; 195(2): 163-70.
[http://dx.doi.org/10.1002/path.947] [PMID: 11592094]
[10]
Tolbert D, Fenoglio-Preiser C, Noffsinger A, et al. The relation of p53 gene mutations to gastric cancer subsite and phenotype. Cancer Causes Control 1999; 10(3): 227-31.
[http://dx.doi.org/10.1023/A:1008899111209] [PMID: 10454068]
[11]
Takiyama H, Ozawa T, Ishihara S, et al. Automatic anatomical classification of esophagogastroduodenoscopy images using deep convolutional neural networks. Sci Rep 2018; 8(1): 7497.
[http://dx.doi.org/10.1038/s41598-018-25842-6] [PMID: 29760397]
[12]
Karargyris A, Bourbakis N. Detection of small bowel polyps and ulcers in wireless capsule endoscopy videos. IEEE Trans Biomed Eng 2011; 58(10): 2777-86.
[http://dx.doi.org/10.1109/TBME.2011.2155064] [PMID: 21592915]
[13]
Mamonov AV, Figueiredo IN, Figueiredo PN, Tsai Y-HR. Automated polyp detection in colon capsule endoscopy. IEEE Trans Med Imaging 2014; 33(7): 1488-502.
[http://dx.doi.org/10.1109/TMI.2014.2314959] [PMID: 24710829]
[14]
Rathore S, Hussain M, Ali A, Khan A. A recent survey on colon cancer detection techniques. IEEE/ACM Trans Comput Biol Bioinformatics 2013; 10(3): 545-63.
[http://dx.doi.org/10.1109/TCBB.2013.84] [PMID: 24091390]
[15]
Iddan G, Meron G, Glukhovsky A, Swain P. Wireless capsule endoscopy. Nature 2000; 405(6785): 417.
[http://dx.doi.org/10.1038/35013140] [PMID: 10839527]
[16]
Mackiewicz M. Capsule Endoscopy-state of the technology and computer vision tools after the first decade, new techniques in gastrointestinal endoscopy. InTech ISBN 2011; pp. 978-53.
[17]
Mackiewicz M. Capsule endoscopy-State of the technology and computer vision tools after the first decade: InTech 2011.
[18]
Hussain H, Lapin S, Cappell MS. Clinical scoring systems for determining the prognosis of gastrointestinal bleeding. Gastroenterol Clin North Am 2000; 29(2): 445-64.
[http://dx.doi.org/10.1016/S0889-8553(05)70122-9] [PMID: 10836189]
[19]
Katz L, Ed. The role of surgery in occult gastrointestinal bleeding Seminars in gastrointestinal disease 1999.
[20]
Triester SL, Leighton JA, Leontiadis GI, et al. A meta-analysis of the yield of capsule endoscopy compared to other diagnostic modalities in patients with obscure gastrointestinal bleeding. Am J Gastroenterol 2005; 100(11): 2407-18.
[http://dx.doi.org/10.1111/j.1572-0241.2005.00274.x] [PMID: 16279893]
[21]
Lewis BS, Swain P. Capsule endoscopy in the evaluation of patients with suspected small intestinal bleeding: Results of a pilot study. Gastrointest Endosc 2002; 56(3): 349-53.
[http://dx.doi.org/10.1016/S0016-5107(02)70037-0] [PMID: 12196771]
[22]
Kalpathy-Cramer J. Classification and retrieval of endoscopic images from the clinical outcomes research initiative (CORI) collection 2009.
[23]
Qureshi WA. Current and future applications of the capsule camera. Nat Rev Drug Discov 2004; 3(5): 447-50.
[http://dx.doi.org/10.1038/nrd1385] [PMID: 15136791]
[24]
Swain P. Wireless capsule endoscopy and Crohn’s disease. Gut 2005; 54(3): 323-6.
[http://dx.doi.org/10.1136/gut.2004.047282] [PMID: 15710975]
[25]
Burke CA, Santisi J, Church J, Levinthal G. The utility of capsule endoscopy small bowel surveillance in patients with polyposis. Am J Gastroenterol 2005; 100(7): 1498-502.
[http://dx.doi.org/10.1111/j.1572-0241.2005.41506.x] [PMID: 15984971]
[26]
Culliford A, Daly J, Diamond B, Rubin M, Green PH. The value of wireless capsule endoscopy in patients with complicated celiac disease. Gastrointest Endosc 2005; 62(1): 55-61.
[http://dx.doi.org/10.1016/S0016-5107(05)01566-X] [PMID: 15990820]
[27]
Ries LA, Wingo PA, Miller DS, et al. The annual report to the nation on the status of cancer, 1973-1997, with a special section on colorectal cancer. Cancer 2000; 88(10): 2398-424.
[http://dx.doi.org/10.1002/(SICI)1097-0142(20000515)88:10<2398::AID-CNCR26>3.0.CO;2-I] [PMID: 10820364]
[28]
Moglia A, Menciassi A, Schurr MO, Dario P. Wireless capsule endoscopy: from diagnostic devices to multipurpose robotic systems. Biomed Microdevices 2007; 9(2): 235-43.
[http://dx.doi.org/10.1007/s10544-006-9025-3] [PMID: 17160703]
[29]
Gheorghe C, Iacob R, Bancila I. Olympus capsule endoscopy for small bowel examination. J Gastrointestin Liver Dis 2007; 16(3): 309-13.
[http://dx.doi.org/10.15403/jgld.2014.1121.263.rom] [PMID: 17925927]
[30]
Bang S, Park JY, Jeong S, et al. First clinical trial of the “MiRo” capsule endoscope by using a novel transmission technology: electric-field propagation. Gastrointest Endosc 2009; 69(2): 253-9.
[http://dx.doi.org/10.1016/j.gie.2008.04.033] [PMID: 18640676]
[31]
Liao Z, Gao R, Li F, et al. Fields of applications, diagnostic yields and findings of OMOM capsule endoscopy in 2400 Chinese patients. World J Gastroenterol 2010; 16(21): 2669-76.
[http://dx.doi.org/10.3748/wjg.v16.i21.2669] [PMID: 20518090]
[32]
Tal AO, Vermehren J, Albert JG. Colon capsule endoscopy: current status and future directions. World J Gastroenterol 2014; 20(44): 16596-602.
[http://dx.doi.org/10.3748/wjg.v20.i44.16596] [PMID: 25469027]
[33]
Liang H, Guan Y, Xiao Z, Hu C, Liu Z, Eds. A screw propelling capsule robot. 2011 IEEE International Conference on Information and Automation.
[http://dx.doi.org/10.1109/ICINFA.2011.5949101]
[34]
Glass P, Cheung E, Sitti M. A legged anchoring mechanism for capsule endoscopes using micropatterned adhesives. IEEE Trans Biomed Eng 2008; 55(12): 2759-67.
[http://dx.doi.org/10.1109/TBME.2008.2002111] [PMID: 19126455]
[35]
Sliker LJ, Schoen JA, Rentschler ME, Eds. Preliminary in vivo capsule crawler mobility. Proc ASME Des Eng Tech Conf.
[36]
Kim B, Lee MG, Lee YP, Kim Y, Lee G. An earthworm-like micro robot using shape memory alloy actuator. Sens Actuators A Phys 2006; 125(2): 429-37.
[http://dx.doi.org/10.1016/j.sna.2005.05.004]
[37]
Park H, Park S, Yoon E, Kim B, Park J, Park S, Eds. Paddling based microrobot for capsule endoscopes. Proceedings 2007 IEEE International Conference on Robotics and Automation.
[http://dx.doi.org/10.1109/ROBOT.2007.363994]
[38]
Carpi F, Galbiati S, Carpi A. Controlled navigation of endoscopic capsules: concept and preliminary experimental investigations. IEEE Trans Biomed Eng 2007; 54(11): 2028-36.
[http://dx.doi.org/10.1109/TBME.2007.894729] [PMID: 18018698]
[39]
Nawarathna R, Oh J, Muthukudage J, et al. Abnormal image detection in endoscopy videos using a filter bank and local binary patterns. Neurocomputing 2014; 144: 70-91.
[http://dx.doi.org/10.1016/j.neucom.2014.02.064] [PMID: 25132723]
[40]
Liedlgruber M, Uhl A. Computer-aided decision support systems for endoscopy in the gastrointestinal tract: a review. IEEE Rev Biomed Eng 2011; 4: 73-88.
[http://dx.doi.org/10.1109/RBME.2011.2175445] [PMID: 22273792]
[41]
Cobrin GM, Pittman RH, Lewis BS. Increased diagnostic yield of small bowel tumors with capsule endoscopy. Cancer 2006; 107(1): 22-7.
[http://dx.doi.org/10.1002/cncr.21975] [PMID: 16736516]
[42]
Chen Y, Lee J, Eds. Ulcer detection in wireless capsule endoscopy video. Proceedings of the 20th ACM international conference on Multimedia.
[http://dx.doi.org/10.1145/2393347.2396413]
[43]
Rajivegandhi C, Shree ND, Khan S, Abinaya B. Detection of peptic ulcers based on thresholding and watershed segmentation. Signal Processing, Communication and Networking (ICSCN), 2015 3rd International Conference on; 2015: IEEE.
[http://dx.doi.org/10.1109/ICSCN.2015.7219910]
[44]
Penna B, Tillo T, Grangetto M, Magli E, Olmo G. A technique for blood detection in wireless capsule endoscopy images. Signal Processing Conference, 2009 17th European; 2009: IEEE.
[45]
Fu Y, Mandal M, Guo G. Bleeding region detection in WCE images based on color features and neural network. Circuits and Systems (MWSCAS), 2011 IEEE 54th International Midwest Symposium.
[http://dx.doi.org/10.1109/MWSCAS.2011.6026527]
[46]
Giritharan B, Yuan X, Liu J, Buckles B, Oh J, Tang SJ. Bleeding detection from capsule endoscopy videos. Engineering in Medicine and Biology Society, 2008 EMBS 2008 30th Annual International Conference of the IEEE.
[http://dx.doi.org/10.1109/IEMBS.2008.4650282]
[47]
Suman S, Malik AS, Riegler M, Ho SH, Hilmi I, Goh KL, Eds. Detection and Classification of Bleeding Region in WCE Images using Color Feature. Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing.
[http://dx.doi.org/10.1145/3095713.3095731]
[48]
Yuan Y, Li B, Meng MQ-H. Improved bag of feature for automatic polyp detection in wireless capsule endoscopy images. IEEE Trans Autom Sci Eng 2016; 13(2): 529-35.
[http://dx.doi.org/10.1109/TASE.2015.2395429]
[49]
Yuan Y, Li B, Meng MQ-H. Bleeding frame and region detection in the wireless capsule endoscopy video. IEEE J Biomed Health Inform 2016; 20(2): 624-30.
[http://dx.doi.org/10.1109/JBHI.2015.2399502] [PMID: 25675468]
[50]
Liu G, Yan G, Kuang S, Wang Y. Detection of small bowel tumor based on multi-scale curvelet analysis and fractal technology in capsule endoscopy. Comput Biol Med 2016; 70: 131-8.
[http://dx.doi.org/10.1016/j.compbiomed.2016.01.021] [PMID: 26829705]
[51]
Mathew M, Gopi VP. Transform based bleeding detection technique for endoscopic images. Electronics and Communication Systems (ICECS), 2015 2nd International Conference on; 2015: IEEE.
[http://dx.doi.org/10.1109/ECS.2015.7124882]
[52]
Eskandari H, Talebpour A, Alizadeh M, Soltanian-Zadeh H. Polyp detection in Wireless Capsule Endoscopy images by using region-based active contour model. Biomedical Engineering (ICBME), 2012 19th Iranian Conference of; 2012: IEEE.
[http://dx.doi.org/10.1109/ICBME.2012.6519699]
[53]
Dilna C, Gopi VP. A novel method for bleeding detection in Wireless Capsule Endoscopic images. Computing and Network Communications (CoCoNet), 2015 International Conference on; 2015: IEEE.
[http://dx.doi.org/10.1109/CoCoNet.2015.7411289]
[54]
Vieira PM, Ramos J, Lima CS. Automatic detection of small bowel tumors in endoscopic capsule images by ROI selection based on discarded lightness information. Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE; 2015: IEEE.
[http://dx.doi.org/10.1109/EMBC.2015.7319029]
[55]
Li B, Meng MQ-H. Automatic polyp detection for wireless capsule endoscopy images. Expert Syst Appl 2012; 39(12): 10952-8.
[http://dx.doi.org/10.1016/j.eswa.2012.03.029]
[56]
Lee Y-G, Yoon G. Real-time image analysis of capsule endoscopy for bleeding discrimination in embedded system platform. World Acad Sci Eng Technol 2011; 59: 2526-30.
[57]
Sainju S, Bui FM, Wahid KA. Automated bleeding detection in capsule endoscopy videos using statistical features and region growing. J Med Syst 2014; 38(4): 25.
[http://dx.doi.org/10.1007/s10916-014-0025-1] [PMID: 24696394]
[58]
Signorelli C, Villa F, Rondonotti E, Abbiati C, Beccari G, de Franchis R. Sensitivity and specificity of the suspected blood identification system in video capsule enteroscopy. Endoscopy 2005; 37(12): 1170-3.
[http://dx.doi.org/10.1055/s-2005-870410] [PMID: 16329012]
[59]
Szczypiński P, Klepaczko A, Pazurek M, Daniel P. Texture and color based image segmentation and pathology detection in capsule endoscopy videos. Comput Methods Programs Biomed 2014; 113(1): 396-411.
[http://dx.doi.org/10.1016/j.cmpb.2012.09.004] [PMID: 23164524]
[60]
Hwang S, Oh J, Cox J, Tang SJ, Tibbals HF, Eds. Blood detection in wireless capsule endoscopy using expectation maximization clustering Medical Imaging 2006: Image Processing. International Society for Optics and Photonics 2006.
[61]
Ma J, Tillo T, Zhang B, Wang Z, Lim EG. Novel training and comparison method for blood detection in wireless capsule endoscopy images. Medical Information and Communication Technology (ISMICT), 2013 7th International Symposium on; 2013: IEEE.
[62]
Pan G, Yan G, Qiu X, Cui J. Bleeding detection in wireless capsule endoscopy based on probabilistic neural network. J Med Syst 2011; 35(6): 1477-84.
[http://dx.doi.org/10.1007/s10916-009-9424-0] [PMID: 20703770]
[63]
Khun PC, Zhuo Z, Yang LZ, Liyuan L, Jiang L. Feature selection and classification for wireless capsule endoscopic frames. Iomedical and Pharmaceutical Engineering, 2009 ICBPE'09 International Conference on; 2009: IEEE.
[http://dx.doi.org/10.1109/ICBPE.2009.5384106]
[64]
Yuan Y, Wang J, Li B, Meng MQ-H. Saliency based ulcer detection for wireless capsule endoscopy diagnosis. IEEE Trans Med Imaging 2015; 34(10): 2046-57.
[http://dx.doi.org/10.1109/TMI.2015.2418534] [PMID: 25850085]
[65]
Souaidi M, Abdelouahed AA, El Ansari M. Multi-scale completed local binary patterns for ulcer detection in wireless capsule endoscopy images. Multimedia Tools Appl 2019; 78(10): 13091-108.
[http://dx.doi.org/10.1007/s11042-018-6086-2]
[66]
Cui L, Hu C, Zou Y, Meng MQ-H. Bleeding detection in wireless capsule endoscopy images by support vector classifier. The 2010 IEEE International Conference on Information and Automation; 2010: IEEE.
[http://dx.doi.org/10.1109/ICINFA.2010.5512218]
[67]
Sivic J, Zisserman A, Eds. Video Google: A text retrieval approach to object matching in videos null. IEEE 2003.
[68]
Li B, Meng MQ-H. Tumor recognition in wireless capsule endoscopy images using textural features and SVM-based feature selection. IEEE Trans Inf Technol Biomed 2012; 16(3): 323-9.
[http://dx.doi.org/10.1109/TITB.2012.2185807] [PMID: 22287246]
[69]
Faigel DO, Cave DR. Capsule endoscopy: Saunders Elsevier 2008.
[70]
Gay G, Delvaux M, Rey J-F. The role of video capsule endoscopy in the diagnosis of digestive diseases: a review of current possibilities. Endoscopy 2004; 36(10): 913-20.
[http://dx.doi.org/10.1055/s-2004-825868] [PMID: 15452790]
[71]
Zhang S, Yang W, Wu Y-L, Yao R, Cheng S-D. Abnormal region detection in gastroscopic images by combining classifiers on neighboring patches. Machine Learning and Cybernetics, 2009 International Conference on; 2009: IEEE.
[http://dx.doi.org/10.1109/ICMLC.2009.5212217]
[72]
Karargyris A, Bourbakis N. Identification of ulcers in wireless capsule endoscopy videos. Biomedical Imaging: From Nano to Macro, 2009 ISBI'09 IEEE International Symposium on; 2009: IEEE.
[http://dx.doi.org/10.1109/ISBI.2009.5193107]
[73]
Li B, Meng MQ-H. Texture analysis for ulcer detection in capsule endoscopy images. Image Vis Comput 2009; 27(9): 1336-42.
[http://dx.doi.org/10.1016/j.imavis.2008.12.003]
[74]
Karargyris A, Bourbakis N. Identification of polyps in wireless capsule endoscopy videos using log gabor filters. Life Science Systems and Applications Workshop, 2009 LiSSA 2009 IEEE/NIH; 2009: IEEE.
[http://dx.doi.org/10.1109/LISSA.2009.4906730]
[75]
Jani KK, Srivastava S, Srivastava R. Computer aided diagnosis system for ulcer detection in capsule endoscopy using optimized feature set. J Intell Fuzzy Syst 2019; 1-8. Preprint
[http://dx.doi.org/10.3233/JIFS-182883]
[76]
Taruna Agrawal RG. Shrikanth Narayanan. On evaluating CNN representations for low resource medical image classification 2019.
[77]
Shanmuga Sundaram P, Santhiyakumari N. An enhancement of computer aided approach for colon cancer detection in WCE images using ROI based color histogram and SVM2. J Med Syst 2019; 43(2): 29.
[http://dx.doi.org/10.1007/s10916-018-1153-9] [PMID: 30612188]
[78]
Obukhova N, Motyko A, Timofeev B, Pozdeev A. Method of endoscopic images analysis for automatic bleeding detection and segmentation 24th Conference of Open Innovations Association (FRUCT); 2019: IEEE
[79]
Sharif M, Khan MA, Iqbal Z, Azam MF, Lali MIU, Javed MY. Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection. Comput Electron Agric 2018; 150: 220-34.
[http://dx.doi.org/10.1016/j.compag.2018.04.023]
[80]
Bokhari F, Syedia T, Sharif M, Yasmin M, Fernandes SL. Fundus image segmentation and feature extraction for the detection of glaucoma: A new approach. Curr Med Imaging Rev 2018; 14(1): 77-87.
[http://dx.doi.org/10.2174/1573405613666170405145913]
[81]
Kiraly AP, Petkov K, Park J-h. Two-dimensional cinematic medical imaging in color based on deep learning. Google Patents 2019.
[82]
Biswas M, Kuppili V, Saba L, et al. State-of-the-art review on deep learning in medical imaging. Front Biosci 2019; 24: 392-426.
[http://dx.doi.org/10.2741/4725] [PMID: 30468663]
[83]
Alaskar H, Hussain A, Al-Aseem N, Liatsis P, Al-Jumeily D. Application of convolutional neural networks for automated ulcer detection in wireless capsule endoscopy images. Sensors (Basel) 2019; 19(6): 1265.
[http://dx.doi.org/10.3390/s19061265] [PMID: 30871162]
[84]
Sharif M, Attique Khan M, Rashid M, Yasmin M, Afza F, Tanik UJ. Deep CNN and geometric features-based gastrointestinal tract diseases detection and classification from wireless capsule endoscopy images. J Exp Theor Artif Intell 2019; 1-23.
[http://dx.doi.org/10.1080/0952813X.2019.1572657]
[85]
Diamantis DE, Iakovidis DK, Koulaouzidis A. Look-behind fully convolutional neural network for computer-aided endoscopy. Biomed Signal Process Control 2019; 49: 192-201.
[http://dx.doi.org/10.1016/j.bspc.2018.12.005]
[86]
Sornapudi S, Meng F, Yi S. Region-Based Automated Localization of Colonoscopy and Wireless Capsule Endoscopy Polyps. Appl Sci (Basel) 2019; 9(12): 2404.
[http://dx.doi.org/10.3390/app9122404]
[87]
Yuan Y, Meng MQH. Deep learning for polyp recognition in wireless capsule endoscopy images. Med Phys 2017; 44(4): 1379-89.
[http://dx.doi.org/10.1002/mp.12147] [PMID: 28160514]
[88]
Billah M, Waheed S. Gastrointestinal polyp detection in endoscopic images using an improved feature extraction method. Biomed Eng Lett 2017; 8(1): 69-75.
[http://dx.doi.org/10.1007/s13534-017-0048-x] [PMID: 30603191]
[89]
Majid A, Khan MA, Yasmin M, Rehman A, Yousafzai A, Tariq U. Classification of stomach infections: A paradigm of convolutional neural network along with classical features fusion and selection. Microsc Res Tech 2020; 83(5): 562-76.
[http://dx.doi.org/10.1002/jemt.23447] [PMID: 31984630]
[90]
Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L, Eds. Imagenet: A large-scale hierarchical image database 2009 IEEE conference on computer vision and pattern recognition. IEEE 2009.
[91]
Ameling S, Wirth S, Paulus D, Lacey G, Vilarino F. Texture-based polyp detection in colonoscopy Bildverarbeitung für die Medizin 2009. Springer 2009; pp. 346-50.
[http://dx.doi.org/10.1007/978-3-540-93860-6_70]
[92]
Owais M, Arsalan M, Choi J, Mahmood T, Park KR. Artificial intelligence-based classification of multiple gastrointestinal diseases using endoscopy videos for clinical diagnosis. J Clin Med 2019; 8(7): 986.
[http://dx.doi.org/10.3390/jcm8070986] [PMID: 31284687]
[93]
Liu S, Liu S, Cai W, Pujol S, Kikinis R, Feng D. Early diagnosis of Alzheimer's disease with deep learning IEEE 11th international symposium on biomedical imaging (ISBI); 2014: IEEE.
[94]
Zou Y, Li L, Wang Y, Yu J, Li Y, Deng W. Classifying digestive organs in wireless capsule endoscopy images based on deep convolutional neural network. IEEE International Conference on; 2015: IEEE.
[http://dx.doi.org/10.1109/ICDSP.2015.7252086]
[95]
Lee Y-G, Yoon G. Bleeding detection algorithm for capsule endoscopy. World Acad Sci Eng Technol 2011; 57.
[96]
Kundu A, Rizve M, Ghosh T, Fattah S, Shahnaz C. A histogram based scheme in YIQ domain for automatic bleeding image detection from wireless capsule endoscopy. Electrical and Computer Engineering (WIECON-ECE), 2015 IEEE International WIE Conference on; 2015: IEEE.
[http://dx.doi.org/10.1109/WIECON-ECE.2015.7443966]
[97]
Deeba F, Bui FM, Wahid KA. Computer-aided polyp detection based on image enhancement and saliency-based selection. Biomed Signal Process Control 2020; 55: 101530.
[http://dx.doi.org/10.1016/j.bspc.2019.04.007]
[98]
Bchir O, Ismail MMB, AlZahrani N. Multiple bleeding detection in wireless capsule endoscopy. Signal Image Video Process 2019; 13(1): 121-6.
[http://dx.doi.org/10.1007/s11760-018-1336-3]
[99]
Yasar A, Saritas I, Korkmaz H. Computer-aided diagnosis system for detection of stomach cancer with image processing techniques. J Med Syst 2019; 43(4): 99.
[http://dx.doi.org/10.1007/s10916-019-1203-y] [PMID: 30874907]
[100]
Pogorelov K, Suman S, Azmadi Hussin F, et al. Bleeding detection in wireless capsule endoscopy videos - Color versus texture features. J Appl Clin Med Phys 2019; 20(8): 141-54.
[http://dx.doi.org/10.1002/acm2.12662] [PMID: 31251460]
[101]
Deeba F, Islam M, Bui FM, Wahid KA. Performance assessment of a bleeding detection algorithm for endoscopic video based on classifier fusion method and exhaustive feature selection. Biomed Signal Process Control 2018; 40: 415-24.
[http://dx.doi.org/10.1016/j.bspc.2017.10.011]
[102]
Ghosh T, Fattah SA, Wahid KA. CHOBS: Color histogram of block statistics for automatic bleeding detection in wireless capsule endoscopy video. IEEE J Transl Eng Health Med 2018; 6: 1800112.
[http://dx.doi.org/10.1109/JTEHM.2017.2756034] [PMID: 29468094]
[103]
Hirasawa T, Aoyama K, Tanimoto T, et al. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer 2018; 21(4): 653-60.
[http://dx.doi.org/10.1007/s10120-018-0793-2] [PMID: 29335825]
[104]
He J-Y, Wu X, Jiang Y-G, Peng Q, Jain R. Hookworm detection in wireless capsule endoscopy images with deep learning. IEEE Trans Image Process 2018; 27(5): 2379-92.
[http://dx.doi.org/10.1109/TIP.2018.2801119] [PMID: 29470172]
[105]
Pogorelov K, Randel KR, Griwodz C, Eskeland SL, de Lange T, Johansen D, Eds. Kvasir: A multi-class image dataset for computer aided gastrointestinal disease detection. Proceedings of the 8th ACM on Multimedia Systems Conference. 2017.
[http://dx.doi.org/10.1145/3083187.3083212]
[106]
Liaqat A, Khan MA, Shah JH, Sharif M, Yasmin M, Fernandes SL. Automated ulcer and bleeding classification from WCE images using multiple features fusion and selection. J Mech Med Biol 2018; 18(04): 1850038.
[http://dx.doi.org/10.1142/S0219519418500380]
[107]
McGoran JJ, McAlindon ME, Iyer PG, Seibel EJ, Haidry R, Lovat LB, et al. Miniature gastrointestinal endoscopy: Now and the future. World J Gastroenterol. 2019; 25(30): 4051.
[108]
Singh S, Urooj S. A methodological approach for analysis of melanoma images. Madridge J Dermatol Res. 2018; 3(2): 83-7.