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For: Ali H, Yasmin M, Sharif M, Rehmani MH. Computer assisted gastric abnormalities detection using hybrid texture descriptors for chromoendoscopy images. Comput Methods Programs Biomed. 2018;157:39-47. [PMID: 29477434 DOI: 10.1016/j.cmpb.2018.01.013] [Cited by in Crossref: 17] [Cited by in F6Publishing: 12] [Article Influence: 4.3] [Reference Citation Analysis]
Number Citing Articles
1 Sharif MI, Li JP, Naz J, Rashid I. A comprehensive review on multi-organs tumor detection based on machine learning. Pattern Recognition Letters 2020;131:30-7. [DOI: 10.1016/j.patrec.2019.12.006] [Cited by in Crossref: 6] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
2 Islam MM, Poly TN, Walther BA, Lin MC, Li YJ. Artificial Intelligence in Gastric Cancer: Identifying Gastric Cancer Using Endoscopic Images with Convolutional Neural Network. Cancers (Basel) 2021;13:5253. [PMID: 34771416 DOI: 10.3390/cancers13215253] [Reference Citation Analysis]
3 Naz J, Sharif M, Raza M, Shah JH, Yasmin M, Kadry S, Vimal S. Recognizing Gastrointestinal Malignancies on WCE and CCE Images by an Ensemble of Deep and Handcrafted Features with Entropy and PCA Based Features Optimization. Neural Process Lett. [DOI: 10.1007/s11063-021-10481-2] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Cao JS, Lu ZY, Chen MY, Zhang B, Juengpanich S, Hu JH, Li SJ, Topatana W, Zhou XY, Feng X, Shen JL, Liu Y, Cai XJ. Artificial intelligence in gastroenterology and hepatology: Status and challenges. World J Gastroenterol 2021; 27(16): 1664-1690 [PMID: 33967550 DOI: 10.3748/wjg.v27.i16.1664] [Cited by in CrossRef: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Lazăr DC, Avram MF, Faur AC, Romoşan I, Goldiş A. The role of computer-assisted systems for upper-endoscopy quality monitoring and assessment of gastric lesions. Gastroenterol Rep (Oxf) 2021;9:185-204. [PMID: 34316369 DOI: 10.1093/gastro/goab008] [Reference Citation Analysis]
6 Zhou K, Cai C, He Y, Zhou C, Zhao S, Ding X, Duan S. Association Between RASSF2 Methylation and Gastric Cancer: A PRISMA-Compliant Systematic Review and Meta-Analysis. DNA Cell Biol 2019;38:1147-54. [PMID: 31453724 DOI: 10.1089/dna.2019.4922] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
7 Hu W, Li C, Li X, Rahaman MM, Ma J, Zhang Y, Chen H, Liu W, Sun C, Yao Y, Sun H, Grzegorzek M. GasHisSDB: A new gastric histopathology image dataset for computer aided diagnosis of gastric cancer. Comput Biol Med 2022;142:105207. [PMID: 35016101 DOI: 10.1016/j.compbiomed.2021.105207] [Reference Citation Analysis]
8 Jin P, Ji X, Kang W, Li Y, Liu H, Ma F, Ma S, Hu H, Li W, Tian Y. Artificial intelligence in gastric cancer: a systematic review. J Cancer Res Clin Oncol. 2020;146:2339-2350. [PMID: 32613386 DOI: 10.1007/s00432-020-03304-9] [Cited by in Crossref: 9] [Cited by in F6Publishing: 10] [Article Influence: 4.5] [Reference Citation Analysis]
9 Goyal H, Sherazi SAA, Mann R, Gandhi Z, Perisetti A, Aziz M, Chandan S, Kopel J, Tharian B, Sharma N, Thosani N. Scope of Artificial Intelligence in Gastrointestinal Oncology. Cancers (Basel) 2021;13:5494. [PMID: 34771658 DOI: 10.3390/cancers13215494] [Reference Citation Analysis]
10 Niu PH, Zhao LL, Wu HL, Zhao DB, Chen YT. Artificial intelligence in gastric cancer: Application and future perspectives. World J Gastroenterol 2020; 26(36): 5408-5419 [PMID: 33024393 DOI: 10.3748/wjg.v26.i36.5408] [Cited by in CrossRef: 12] [Cited by in F6Publishing: 7] [Article Influence: 6.0] [Reference Citation Analysis]
11 Khan MA, Rashid M, Sharif M, Javed K, Akram T. Classification of gastrointestinal diseases of stomach from WCE using improved saliency-based method and discriminant features selection. Multimed Tools Appl 2019;78:27743-70. [DOI: 10.1007/s11042-019-07875-9] [Cited by in Crossref: 17] [Cited by in F6Publishing: 3] [Article Influence: 5.7] [Reference Citation Analysis]
12 Murakami D, Yamato M, Amano Y, Tada T. Challenging detection of hard-to-find gastric cancers with artificial intelligence-assisted endoscopy. Gut. 2020;. [PMID: 32816967 DOI: 10.1136/gutjnl-2020-322453] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
13 Kohoutova D, Banks M, Bures J. Advances in the Aetiology & Endoscopic Detection and Management of Early Gastric Cancer. Cancers (Basel) 2021;13:6242. [PMID: 34944861 DOI: 10.3390/cancers13246242] [Reference Citation Analysis]
14 Jiang K, Jiang X, Pan J, Wen Y, Huang Y, Weng S, Lan S, Nie K, Zheng Z, Ji S, Liu P, Li P, Liu F. Current Evidence and Future Perspective of Accuracy of Artificial Intelligence Application for Early Gastric Cancer Diagnosis With Endoscopy: A Systematic and Meta-Analysis. Front Med (Lausanne) 2021;8:629080. [PMID: 33791323 DOI: 10.3389/fmed.2021.629080] [Reference Citation Analysis]
15 Zhao Y, Hu B, Wang Y, Yin X, Jiang Y, Zhu X. Identification of gastric cancer with convolutional neural networks: a systematic review. Multimed Tools Appl. [DOI: 10.1007/s11042-022-12258-8] [Reference Citation Analysis]
16 Liu D, Rao N, Mei X, Jiang H, Li Q, Luo C, Li Q, Zeng C, Zeng B, Gan T. Annotating Early Esophageal Cancers Based on Two Saliency Levels of Gastroscopic Images. J Med Syst 2018;42:237. [PMID: 30327890 DOI: 10.1007/s10916-018-1063-x] [Cited by in Crossref: 3] [Article Influence: 0.8] [Reference Citation Analysis]
17 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. Journal of Experimental & Theoretical Artificial Intelligence 2021;33:577-99. [DOI: 10.1080/0952813x.2019.1572657] [Cited by in Crossref: 31] [Article Influence: 10.3] [Reference Citation Analysis]
18 Le Berre C, Sandborn WJ, Aridhi S, Devignes MD, Fournier L, Smaïl-Tabbone M, Danese S, Peyrin-Biroulet L. Application of Artificial Intelligence to Gastroenterology and Hepatology. Gastroenterology 2020;158:76-94.e2. [PMID: 31593701 DOI: 10.1053/j.gastro.2019.08.058] [Cited by in Crossref: 108] [Cited by in F6Publishing: 96] [Article Influence: 36.0] [Reference Citation Analysis]