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Designing a Hybrid Method of Artificial Neural Network and Particle Swarm Optimization to Diagnosis Polyps from Colorectal CT Images
Abstract
Background: Since colorectal cancer is one of the most important types of cancer in the world that often leads to death, computer‑aided diagnostic (CAD) systems are a promising solution for early diagnosis of this disease with fewer side effects than conventional colonoscopy. Therefore, the aim of this research is to design a CAD system for processing colorectal Computerized Tomography (CT) images using a combination of an artificial neural network and a particle swarm optimizer. Method: First, the data set of the research was created from the colorectal CT images of the patients of Loghman‑e Hakim Hospitals in Tehran and Al‑Zahra Hospitals in Isfahan who underwent colorectal CT imaging and had conventional colonoscopy done within a maximum period of one month after that. Then the steps of model implementation, including electronic cleansing of images, segmentation, labeling of samples, extraction of features, and training and optimization of the artificial neural network (ANN) with a particle swarm optimizer, were performed. A binomial statistical test and confusion matrix calculation were used to evaluate the model. Results: The values of accuracy, sensitivity, and specificity of the model with a P value = 0.000 as a result of the McNemar test were 0.9354, 0.9298, and 0.9889, respectively. Also, the result of the P value of the binomial test of the ratio of diagnosis of the model and the radiologist from Loqman Hakim and Al‑Zahra Hospitals was 0.044 and 0.021, respectively. Conclusions: The results of statistical tests and research variables show the efficiency of the CTC‑CAD system created based on the hybrid of the ANN and particle swarm optimization compared to the opinion of radiologists in diagnosing colorectal polyps from CTC images.
International Journal of Preventive Medicine 15():4, January 2024. | DOI: 10.4103/ijpvm.ijpvm_373_22
Corresponding Author: Dr. Hossein Beigi Harchegani
E‑mail: beigi@mng.mui.ac.ir
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Chadebecq F, Tilmant C, Bartoli A. How big is this neoplasia?
live colonoscopic size measurement using the infocus‑breakpoint.
Med Image Anal 2015;19:58‑74.
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I,
Jemal A, et al. Global cancer statistics 2020: GLOBOCAN
estimates of incidence and mortality worldwide for 36 cancers in
countries. CA Cancer J Clin 2021;71:209‑49.
Harchegani HB, Moghaddasi H, Shaebani M. Judgement
Comparison between Radiologists and Computer Aided
Diagnosis Systems in Polyps Diagnosis based on Colorectal
CT Images. Journal of Knowledge & Health in Basic Medical
Sciences. 2022;17:52‑8. doi: 10.22100/jkh.v17i2.2727.
Wang Z, Liang Z, Li X, Li L, Li B, Eremina D, et al. An
improved electronic colon cleansing method for detection of
colonic polyps by virtual colonoscopy. IEEE Trans Biomed Eng
;53:1635‑46.
Roth HR, Lu L, Liu J, Yao J, Seff A, Cherry K, et al. Improving
computer‑aided detection using convolutional neural networks
and random view aggregation. IEEE Trans Med Imaging 2016;35:1170‑81.
Wan M, Li WJ, Kreeger K, Bitter I, Kaufman AE, Liang Z, et al.
D virtual colonoscopy with real‑time volume rendering. Medical
Imaging 2000: Physiology and Function from Multidimensional
Images. 2000;3978:165‑71.
Hoppe H, Netzer P, Spreng A, Quattropani C, Mattich J,
Dinkel HP. Prospective comparison of contrast enhanced CT
colonography and conventional colonoscopy for detection
of colorectal neoplasms in a single institutional study using
second‑look colonoscopy with discrepant results. Am J
Gastroenterol 2004;99:1924‑35.
Pohl J, Nguyen‑Tat M, Pech O, May A, Rabenstein T, Ell C.
Computed virtual chromoendoscopy for classification of small
colorectal lesions: A prospective comparative study. Am J
Gastroenterol 2008;103:562‑9.
Zameni M, Sheikholeslami H, Javadi H, Javadi A. Change
of arterial O2 saturation, heart rate, SBP and DBP during
colonoscopy until splenic flexure. Journal of Inflammatory
Diseases 2003;7:38-42.
Leung WK, Lo OS, Liu KS, Tong T, But DY, Lam FY,
et al. Detection of colorectal adenoma by narrow band
imaging (HQ190) vs. high‑definition white light colonoscopy:
A randomized controlled trial. Am J Gastroenterol
;109:855‑63.
Bellini D, De Santis D, Caruso D, Rengo M, Ferrari R,
Biondi T, et al. Bowel preparation in CT colonography: Is diet
restriction necessary? a randomised trial (DIETSAN). Eur Radiol
;28:382‑89.
Chen D, Liang Z, Wax MR, Li L, Li B, Kaufman AE. A novel
approach to extract colon lumen from CT images for virtual
colonoscopy. IEEE Trans Med Imaging 2000;19:1220‑6.
Moghaddasi H, Harchegani HB, Shaebani M, Beigy H,
SalavatiPour B, Improving the Quality of Electronic Cleansing
of Colorectal CT Images Using a Hybrid Method. Journal of
Biomedical Engineering and Medical Imaging 2019;6:09.
Kiss G, Van Cleynenbreugel J, Thomeer M, Suetens P,
Marchal G. Computer‑aided diagnosis in virtual colonography
via combination of surface normal and sphere fitting methods.
Eur Radiol 2002;12:77‑81.
Suh JW, Wyatt CL. Registration under topological change for CT
colonography. IEEE Trans Biomed Eng 2011;58:1403‑11.
Moghaddasi H, Harchegani H. Application of virtual reality in
colonoscopy. J Health Inform Manag 2 2018;1:2.
Suzuki K, Yoshida H, Näppi J, Armato SG 3rd, Dachman AH.
Mixture of expert 3D massive‑training anns for reduction of
multiple types of false positives in cad for detection of polyps in
CT colonography. Med Phys 2008;35:694‑703.
Song B, Zhu H, Zhu W, Liang Z, editors. Evaluation
of classifiers for computer‑aided detection in computed
tomography colonography. 2011 IEEE Nuclear Science
Symposium Conference Record; 2011: IEEE. doi: 10.1109/
NSSMIC.2011.6153732.
Lu L, Wolf M, Bi J, Salganicoff M, editors. Correcting
misalignment of automatic 3D detection by classification:
ileo‑cecal valve false positive reduction in CT colonography.
International MICCAI Workshop on Medical Computer Vision;
: Springer. doi: 10.1007/978-3-642-18421-5_12.
Suzuki K, Zhang J, Xu J. Massive‑training artificial neural
network coupled with laplacian‑eigenfunction‑based
dimensionality reduction for computer‑aided detection of polyps
in CT colonography. IEEE Trans Med Imaging 2010;29:1907‑17.
Ye X, Slabaugh G. A model‑driven Bayesian method
for polyp detection and false positive suppression in CT
colonography Computer‑Aided detection. Machine Learning in
Computer‑Aided Diagnosis: Medical Imaging Intelligence and
Analysis: IGI Global; 2012. p. 220‑37. doi: 10.4018/978-1-46660059-1.ch011.
Shi Z, Ma J, Feng Y, He L, Suzuki K. Evaluation of MTANNs
for eliminating false‑positive with different computer aided
pulmonary nodules detection software. Pak J Pharm Sci
;28(6 Suppl):2311‑6.
Shanmuganathan S. Artificial neural network modelling: An
introduction. Artificial neural network modelling: Springer; 2016.
p. 1‑14.
Poli R, Kennedy J, Blackwell T. Particle swarm optimization.
Swarm intelligence. 2007;1:33‑57.
Zalis ME, Perumpillichira J, Hahn PF. Digital subtraction bowel
cleansing for CT colonography using morphological and linear
filtration methods. IEEE Trans Med Imaging 2004;23:1335‑43.
Meloun M, Militký J. Statistical data analysis: A practical
guide: Woodhead Publishing Limited; 2011. doi:
org/10.1533/9780857097200.
Skalski A, Socha M, Zieliński T, Duplaga M. Virtual
Colonoscopy‑Technical Aspects. Colonoscopy: IntechOpen;
doi: 10.5772/19329.
Sethian JA. A fast marching level set method for monotonically
advancing fronts. Proc Natl Acad Sci U S A 1996;93:1591‑5.
Loftus EV Jr. Clinical epidemiology of inflammatory bowel
disease: Incidence, prevalence, and environmental influences.
Gastroenterology 2004;126:1504‑17.
Jiang R, Berliner L, Meng J. Computer graphics enhancements
in CT Colonography for improved diagnosis and navigation.
International Congress Series 2005;1281:109‑14.
Juillerat P, Mottet C, Froehlich F, Felley C, Vader JP, Burnand B,
et al. Extraintestinal manifestations of Crohn’s disease. Digestion
;71:31‑6.
Bankman I. Handbook of medical image processing and analysis:
Elsevier; 2008. doi: 10.1016/B978-0-12-373904-9.X0001-4.
Azhagusundari B, Thanamani AS. Feature selection based on
information gain. International Journal of Innovative Technology
and Exploring Engineering (IJITEE). 2013;2:18‑21.