Age-based computer-aided diagnosis approach for pancreatic cancer on endoscopic ultrasound images

Endoscopic Ultrasound

Volumn 5, Issue 2, 2016, Pages 101-107

  Ozkan, Murat ^a,b   Cakiroglu, Murat ^b   Kocaman, Orhan ^c   Kurt, Mevlut ^d   Yilmaz, Bulent ^d   Can, Guray ^d   Korkmaz, Ugur ^d   Dandil, Emre ^b,e   Eksi, Ziya ^b  

^a Bolu Vocational School   (Turkey)

^b SAKARYA UNIVERSITY   (Turkey)

^c BEZMIALEM VAKIF UNIVERSITY   (Turkey)

^d ABANT IZZET BAYSAL UNIVERSITY   (Turkey)

^e BILECIK SEYH EDEBALI UNIVERSITY   (Turkey)

Author keywords

Computer aided diagnosis (CAD); Endoscopic ultrasound (EUS) images; Pancreatic cancer

Indexed keywords

ADOLESCENT; ADULT; AGE; AGED; ARTICLE; ARTIFICIAL NEURAL NETWORK; CANCER CLASSIFICATION; CANCER DIAGNOSIS; COMPUTER ASSISTED DIAGNOSIS; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; ENDOSCOPIC ULTRASONOGRAPHY; FEMALE; HUMAN; IMAGE PROCESSING; INTERMETHOD COMPARISON; MAJOR CLINICAL STUDY; MALE; PANCREAS CANCER; PATTERN RECOGNITION; SENSITIVITY AND SPECIFICITY; VERY ELDERLY;

EID: 85002145930     PISSN: 23039027     EISSN: 22267190     Source Type: Journal    
DOI: 10.4103/2303-9027.180473     Document Type: Article

References (18)

1. Siegel R, Ma J, Zou Z, et al. Cancer statistics, 2014. CA Cancer J Clin 2014;64:9-29
2. Hidalgo M. Pancreatic cancer. N Engl J Med 2010;362:1605-17
3. DiMagno EP, Malagelada JR, Taylor WF, et al. A prospective comparison of current diagnostic tests for pancreatic cancer. N Engl J Med 1977;297:737-42
4. Gudjonsson B. Cancer of the pancreas. 50 years of surgery. Cancer 1987;60:2284-303
5. Freeny PC, Marks WM, Ryan JA, et al. Pancreatic ductal adenocarcinoma: Diagnosis and staging with dynamic CT. Radiology 1988;166:125-33
6. Zhang MM, Yang H, Jin ZD, et al. Diff erential diagnosis of pancreatic cancer from normal tissue with digital imaging processing and pattern recognition based on a support vector machine of EUS images. Gastrointest Endosc 2010;72:978-85
7. Aslantas A, Dandil E, Saglam S, et al. CADBOSS: A computer-aided diagnosis system for whole-body bone scintigraphy. J Cancer Res Ther. 2015. [In Press]
8. Zhu M, Xu C, Yu J, et al. Differentiation of pancreatic cancer and chronic pancreatitis using computer-aided diagnosis of endoscopic ultrasound (EUS) images: A diagnostic test. PLoS One 2013;8:e63820
9. Das A, Nguyen CC, Li F, et al. Digital image analysis of EUS images accurately diff erentiates pancreatic cancer from chronic pancreatitis and normal tissue. Gastrointest Endosc 2008;67:861-7
10. Rajan E, Clain JE, Levy MJ, et al. Age-related changes in the pancreas identifi ed by EUS: A prospective evaluation. Gastrointest Endosc 2005;61:401-6
11. Haralick RM, Shanmugam K, Dinstein I. Textural features for image classifi cation. IEEE Trans Syst Man Cybern 1973;SMC-3:610-21
12. Clausi DA. An analysis of co-occurrence texture statistics as a function of grey level quantization. Can J Remote Sensing 2002;28:45-62
13. Akilandeswari U, Nithya R, Santhi B. Review on feature extraction methods in pattern classifi cation. Eur J Sci Res 2012;71:265-72
14. Van de Wouwer G, Scheunders P, Van Dyck D. Statistical texture characterization from discrete wavelet representation. IEEE Trans Image Process 1999;8:592-8
15. Tahir F, Fahiem MA. A statistical-textural-features based approach for classifi cation of solid drugs using surface microscopic images. Comput Math Methods Med 2014;2014:791246
16. Kira K, Rendell LA. The feature selection problem: Traditional methods and a new algorithm. Amer Assoc Artifi cial I 1992;129-34
17. Dandil E, Çevik KK. Yapay sinir ağlari için net platformunda görsel bir egitim yaziliminin gelistirilmesi. International Journal of Informatics Technologies 2012;5:19-28
18. Fritscher-Ravens A, Brand L, Knöfel WT, et al. Comparison of endoscopic ultrasound-guided fine needle aspiration for focal pancreatic lesions in patients with normal parenchyma and chronic pancreatitis. Am J Gastroenterol 2002; 97:2768-75