Improved Particle Swarm Optimization for Detection of Pancreatic Tumor using Split and Merge Algorithm

References

• Alkeshuosh AH, Moghadam MZ, Al Mansoori I, Abdar M. 2017. Using PSO algorithm for producing best rules in diagnosis of heart disease. International conference on computer and applications (ICCA), Qatar; Sep 6; IEEE. p. 306–311.
   Google Scholar
• Allioui H, Sadgal M, Elfazziki A. 2021 Jan. Optimized control for medical image segmentation: improved multi-agent systems agreements using particle swarm optimization. J Ambient Intell Humaniz Comput. 21:1–9.
   Google Scholar
• Arulmozhi S, Shankar R, Duraisamy S. 2020 Aug. A review: deep learning techniques for image classification of pancreatic tumor. ICTACT J Image Video Process. 10(1):2217–2223.
   Google Scholar
• Arulmozhi S, Shankar R, Duraisamy S. 2021 May. Detection and diagnosis of pancreatic tumor using convolutional neural network and hybrid particle swarm optimization algorithm (CNN-HPSO) for image classification. Ann Roman Soc Cell Biol. 4:1403–1419.
   Google Scholar
• Bik M, Elbadawi NE. 2018. Registration of MRI-CT image for improving abdominal tumor localization using particle swarm optimization [Doctoral dissertation]. University of Gezira.
   Google Scholar
• Chu LC, Goggins MG, Fishman EK. 2017 Nov 1. Diagnosis and detection of pancreatic cancer. Cancer J. 23(6):333–342. doi10.1097/PPO.0000000000000290.
   Google Scholar
• Chu LC, Park S, Kawamoto S, Wang Y, Zhou Y, Shen W, Zhu Z, Xia Y, Xie L, Liu F, et al. 2019 Sep 1. Application of deep learning to pancreatic cancer detection: lessons learned from our initial experience. J Am Coll Radiol. 16(9):1338–1342. doi10.1016/j.jacr.2019.05.034.
   Google Scholar
• Chuang LY, Yang CH, Yang CH. 2009 Dec 1. Tabu search and binary particle swarm optimization for feature selection using microarray data. J comput biol. 16(12):1689–1703. doi10.1089/cmb.2007.0211.
   Google Scholar
• Cruz-Aceves I, Aviña-Cervantes JG, López-Hernández JM, González-Reyna SE. 2013 Apr. Multiple active contours driven by particle swarm optimization for cardiac medical image segmentation. Comput Math Methods Med: Vol. 2013. doi: 10.1155/2013/132953
   Google Scholar
• DeWitt J, Devereaux B, Chriswell M, McGreevy K, Howard T, Imperiale TF, Ciaccia D, Lane KA, Maglinte D, Kopecky K, et al. 2004 Nov 16. Comparison of endoscopic ultrasonography and multidetector computed tomography for detecting and staging pancreatic cancer. Ann Intern Med. 141(10):753–763. doi10.7326/0003-4819-141-10-200411160-00006.
   Google Scholar
• Dhruv B, Mittal N, Modi M. 2018. Comparative analysis of edge detection techniques for medical images of different body parts. In: Panda B, Sharma S, Roy N, editors. Data science and analytics. REDSET 2017. Communications in computer and information science. Vol. 799. Singapore: Springer; p. 164–176.
   Google Scholar
• Dhruv B, Mittal N, Modi M. 2019 Apr 3. Study of Haralick’s and GLCM texture analysis on 3D medical images. Intern J Neurosci. 129(4):350–362. doi10.1080/00207454.2018.1536052.
   Google Scholar
• Dhruv B, Mittal N, Modi M. 2021. Early and precise detection of pancreatic tumor by hybrid approach with edge detection and artificial intelligence techniques. Phat EAI. doi:10.4108/eai.31-5-2021.170009.
   Google Scholar
• Gambhir S, Malik SK, Kumar Y. 2017 Nov 1. PSO-ANN based diagnostic model for the early detection of dengue disease. New Horiz Transl Med. 4(1–4):1–8. doi10.1016/j.nhtm.2017.10.001.
   Google Scholar
• Ghanad NK, Ahmadi S. 2015 Jul. Combination of PSO algorithm and naive Bayesian classification for Parkinson disease diagnosis. Adv Comput Sci an Int J. 4(4):119–125.
   Google Scholar
• Gunasundari S, Janakiraman S, Meenambal S. 2018 Dec 1. Multiswarm heterogeneous binary PSO using win-win approach for improved feature selection in liver and kidney disease diagnosis. Comput Med Imaging Graph. 70:135–154. doi:10.1016/j.compmedimag.2018.10.003.
   Google Scholar
• Joloudari JH, Saadatfar H, Dehzangi A, Shamshirband S. 2019 Jan 1. Computer-aided decision-making for predicting liver disease using PSO-based optimized SVM with feature selection. Inform Med Unlocked. 17:100255. doi:10.1016/j.imu.2019.100255.
   Google Scholar
• Lee ES, Lee JM. 2014 Jun 28. Imaging diagnosis of pancreatic cancer: a state-of-the-art review. WJG. 20(24):7864. doi10.3748/wjg.v20.i24.7864.
   Google Scholar
• Li Y, Bai X, Jiao L, Xue Y. 2017 Jul 1. Partitioned-cooperative quantum-behaved particle swarm optimization based on multilevel thresholding applied to medical image segmentation. Appl Soft Comput. 56:345–356. doi:10.1016/j.asoc.2017.03.018.
   Google Scholar
• Li Y, Jiao L, Shang R, Stolkin R. 2015 Feb 10. Dynamic-context cooperative quantum-behaved particle swarm optimization based on multilevel thresholding applied to medical image segmentation. Inf Sci (Ny). 294:408–422. doi:10.1016/j.ins.2014.10.005.
   Google Scholar
• Liu X, Fu H. 2014 May. PSO-based support vector machine with cuckoo search technique for clinical disease diagnoses. Sci World J. 25:2014.
   Google Scholar
• Meghdadi N, Soltani M, Niroomand-Oscuii H, Yamani N. 2020 Feb. Personalized image-based tumor growth prediction in a convection–diffusion–reaction model. Acta Neurol Belg. 120(1):49–57. doi:10.1007/s13760-018-0973-1.
   Google Scholar
• Mello Román JC, Vázquez Noguera JL, Legal-Ayala H, Pinto-Roa DP, Gomez-Guerrero S, García Torres M. 2019 Mar. Entropy and contrast enhancement of infrared thermal images using the multiscale top-hat transform. Entropy. 21(3):244. doi:10.3390/e21030244.
   Google Scholar
• Mittal N. 2012. Automatic contrast enhancement of low contrast images using MATLAB. Int J Adv Res Comput Sci. 3(1):333–338.
   Google Scholar
• Mittal N, Singh HP, Gupta R. 2015. Decomposition & reconstruction of medical images in MATLAB using different Wavelet parameters. 2015 International Conference on Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE), India; Feb 25; IEEE. p. 647–653.
   Google Scholar
• Mittal N, Tanwar S, Khatri SK. 2017. Identification & enhancement of different skin lesion images by segmentation techniques. 2017 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO), India; Sep 20; IEEE. p. 609–614.
   Google Scholar
• Mohammed ZF, Abdulla AA. 2020 Feb 13. UHD. J Sci Technol. 4(1):9–17.
   Google Scholar
• Pal JK, Ray SS, Pal SK. 2017 Oct 1. Fuzzy mutual information based grouping and new fitness function for PSO in selection of miRNAs in cancer. Comput Biol Med. 89:540–548. doi:10.1016/j.compbiomed.2017.08.013.
   Google Scholar
• Pereira SP, Oldfield L, Ney A, Hart PA, Keane MG, Pandol SJ, Li D, Greenhalf W, Jeon CY, Koay EJ, et al. 2020 Jul 1. Early detection of pancreatic cancer. Lancet Gastroenterol Hepatol. 5(7):698–710. doi10.1016/S2468-1253(19)30416-9.
   Google Scholar
• Raja JB, Pandian SC. 2020 Nov 1. PSO-FCM based data mining model to predict diabetic disease. Comput Methods Programs Biomed. 196:105659. doi:10.1016/j.cmpb.2020.105659.
   Google Scholar
• Tahir MA, Bouridane A, Kurugollu F. 2005 May. An FPGA based coprocessor for GLCM and Haralick texture features and their application in prostate cancer classification. Analog Integr Circuits Signal Process. 43(2):205–215. doi:10.1007/s10470-005-6793-2.
   Google Scholar
• Thanh DN, Erkan U, Prasath VS, Kumar V, Hien NN. 2019. A skin lesion segmentation method for dermoscopic images based on adaptive thresholding with normalization of color models. 2019 6th International Conference on Electrical and Electronics Engineering (ICEEE), Turkey; Apr 16; IEEE. p. 116–120.
   Google Scholar
• Zeng N, Qiu H, Wang Z, Liu W, Zhang H, Li Y. 2018 Dec 3. A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer’s disease. Neurocomputing. 320:195–202. doi:10.1016/j.neucom.2018.09.001.
   Google Scholar
• Zhang Y, Ji G, Yang J, Wang S, Dong Z, Phillips P, Sun P. 2016 Jan 1. Preliminary research on abnormal brain detection by wavelet-energy and quantum-behaved PSO. Technol Health Care. 24(s2):S641–9. doi10.3233/THC-161191.
   Google Scholar
• Zhang Y, Wang S, Sui Y, Yang M, Liu B, Cheng H, Sun J, Jia W, Phillips P, Gorriz JM. 2018 Jan 1. Multivariate approach for Alzheimer’s disease detection using stationary wavelet entropy and predator-prey particle swarm optimization. J Alzheimer’s Dis. 65(3):855–869. doi10.3233/JAD-170069.
   Google Scholar
• Zhao W, Shen L, Han B, Yang Y, Cheng K, Toesca DA, Koong AC, Chang DT, Xing L. 2019 Oct 1. Markerless pancreatic tumor target localization enabled by deep learning. Int J Radiat Oncol Biol Phys. 105(2):432–439. doi10.1016/j.ijrobp.2019.05.071.
   Google Scholar
• Zhu Z, Xia Y, Xie L, Fishman EK, Yuille AL. 2019. Multi-scale coarse-to-fine segmentation for screening pancreatic ductal adenocarcinoma. International conference on medical image computing and computer-assisted intervention; Oct 13; Cham: Springer.p. 3–12).
   Google Scholar