ارائه روشی جدید برای آشکارسازی سرطان سینه در تصاویر ماموگرافی با استفاده از الگوریتم کرم شب تاب
الموضوعات :
غزال مردانیان
1
(
کارشناس ارشد - دانشکده مهندسی برق، واحد نجف‌آباد، دانشگاه آزاد اسلامی، نجف‌آباد، ایران
)
ندا بهزادفر
2
(
استادیار - دانشکده مهندسی برق، واحد نجف‌آباد، دانشگاه آزاد اسلامی، نجف‌آباد، ایران
)
الکلمات المفتاحية: مورفولوژی, سرطان سینه, الگوریتم کرم شب‌تاب, تصاویر ماموگرافی دیجیتال,
ملخص المقالة :
سرطان سینه یکی از شایع ترین سرطان ها در بین زنان است. در بسیاری از مواقع، هیچ علائم آشکاری در بیماران مبتلا به سرطان سینه مشاهده نمی شود. تشخیص دقیق سرطان سینه در مراحل اولیه برای کاهش مرگ و میر امری ضروری است. ماموگرافی به عنوان یک روش استاندارد بیش از 40 سال است که در تشخیص بیماری های سینه مورد استفاده قرار گرفته است. برای جلوگیری از تجزیه و تحلیل های ذهنی تصاویر ماموگرافی توسط رادیولوژیست ها و افزایش دقت آشکارسازی سرطان سینه، سیستم های مبتنی بر هوش مصنوعی در سال های اخیر مورد توجه زیادی قرار گرفته اند. در این مطالعه با ترکیب الگوریتم کرم شب تاب و اعمال پیش پردازش های مناسب بر روی تصویر به آشکارسازی سرطان سینه در تصاویر ماموگرافی پرداخته شده است. در این مطالعه، از تصاویر ماموگرافی موجود در مجموعه داده DDSM استفاده شد. 3 معیار عملکردی صحت، حساسیت و دقت (%4/93، 91%، 95% ) برای تجزیه و تحلیل عملکرد تشخیص استفاده شد. اثر پیشنهادی در مقایسه با کارهای موجود در ادبیات عملکرد بهتری نشان می دهد
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[30] H. R. Javadi , H. Pourghasem, "kin lesion classification from dermoscopy images using color and shape features", Journal of Intelligent Procedures in Electrical Technology, Vol. 8, No. 29, pp. 33-40, Spring 2017.
_||_[1] B. Milovic, "Prediction and decision making in health care using data mining", Kuwait Chapter of Arabian Journal of Business and Management Review, Vol. 1, No, 12, pp. 126-136, 2012.
[2] M. H. M. Adnan, W. Husain, N. A. Rashid, "Data mining for medical systems: A review." Proceedings International Conference on Advances in Computer and Information Technology-ACIT. 2012 (doi: 10.3850/978-981-07-3161-8_ACIT-170).
[3] V. Rafe, R. H. Farhoud, "A survey on data mining approaches in medicine", International Research Journal of Applied and Basic Science, Vol. 4, No. 1, pp. 196-202, 2013.
[4] Y. Cho, C. Chin, K. Wang, "Based on fuzzy linear discriminant analysis for breast cancer mammography analysis", Proceeding of the IEEE/TAAI, Chung-Li, Taiwan, Noc. 2011 (doi:10.1109/TAAI.2011.18).
[5] D. A. Schauer, W. L. Otha, "National council on radiation protection and measurements report shows substantial medical exposure increase", Radiology, Vol. 253, No. 2, pp. 293-296, 2009 (doi:10.1148/radiol.2532090494).
[6] J. Ferlay, S. HR, F. Bray, D. Forman, C. Mathers, D, M. Parkin, "Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008", International Journal of Cancer, Vol. 127, No. 12, pp. 2893-2917, Dec. 2010 (doi:10.1002/ijc. 25516.).
[7] H. Mahmoodian, S. Nasr, "Insulin drug regulation by general type 2 fuzzy controller with alpha plane", Journal of Intelligent Procedures in Electrical Technology, Vol. 10, No. 37, pp. 39-48, Spring 2019.
[8] F. Hourali, M. Momeny, S. Gharrav, "Reducing the impact of SYN flood attacks by improving the accuracy of the PSO algorithm by adaptive effective filters", Journal of Intelligent Procedures in Electrical Technology, Vol. 10, No. 37, pp. 51-57, Spring 2019.
[9] S. Runjie, Y. Yang, and F. Shao. "Intelligent breast cancer prediction model using data mining techniques." Proceedings of the IEEE sixth international conference on Intelligent Human-Machine Systems and Cybernetics, Hangzhou, China, pp. 384-387. 2014 (doi: 10.1109/IHMSC.2014.100).
[10] P. S. Pawar, and D. R. Patil. "Breast cancer detection using neural network models", Proceedings of the IEEE/ Communication Systems and Network Technologies , Gwalior, India, pp. 568-572.2013 (doi: 10.1109/CSNT. 2013.122).
[11] M. Sadeghzadeh, "A New Method for Diagnosing Breast Cancer using Firefly Algorithm and Fuzzy Rule based Classification", Proceedings of the IEEE/AICT , Moscow, Russia, pp. 1-5, 2017 (doi: 10.1109/ICAICT. 2017.8687061).
[12] S. Ghosh, S. Mondal, B. Ghosh, "A comparative study of breast cancer detection based on SVM and MLP BPN classifier", Proceedings of the IEEE/ACES, pp. 1-4, Hooghy, India, 2014 (doi: 10.1109/ACES.2014.6808002).
[13] M. R. Yousefi, "Magnetic induction tomography: a review of process and medical tomography systems", Journal of Intelligent Procedures in Electrical Technology, Vol. 8, No. 31, pp. 33-50, Autumn 2017.
[14] G. Shahgholian, A. Movahedi, "Modeling and controller design using ANFIS method for non-linear liquid level system", International Journal of Information and Electronics Engineering, Vol. 1, No. 3, pp. 271-275, Nov. 2011 (doi:10.7763/IJIEE.2011.V1.43).
[15] N. Behzadfar, S. M. P. Firoozabadi, K. Badie, "Low-complexity discriminative feature selection from eeg before and after short-term memory task", Clinical EEG and neuroscience, Vol. 47, No. 4, pp. 291-297, Feb. 2016 (doi:10.1177/1550059416633951).
[16] D. Karaboga, B. Basturk, "A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm", Journal of Global Optimization, Vol. 39, No. 3, pp. 459-471, Nov. 2007 (doi: 10.1007/s 10898-007-9149-x).
[17] G. Shahgholian, S. Fazeli-Nejad, M. Moazzami, M. Mahdavian, M. Azadeh, M. Janghorbani, S. Farazpey, "Power system oscillations damping by optimal coordinated design between PSS and STATCOM using PSO and ABC algorithms", Proceeding of the IEEE/ECTICON, Chiang Mai, Thailand, pp. 1-6, June/July 2016 (doi:10.1109/ECTICon.2016.7561458).
[18] M. Dorigo, T. Stützle, "The ant colony optimization metaheuristic: Algorithms, applications, and advances", Handbook of Metaheuristics, Springer, Boston, MA, pp. 250-285, 2003 (doi:10.1007/0-306-48056-5_9).
[19] M. Lotfi-Forushani, B. Karimi, G. Shahgholian, "Optimal PID controller tuning for multivariable aircraft longitudinal autopilot based on particle swarm optimization algorithm", Journal of Intelligent Procedures in Electrical Technology, Vol. 3, No. 9, pp. 41-50, Spring 2012.
[20] X. S. Yang, "A new metaheuristic bat-inspired algorithm", Nature Inspired Cooperative Strategies for Optimization, Springer, Berlin, Heidelberg, pp. 65-74, 2010 (doi:10.1007/978-3-642-12538-6_6).
[21] A. H. Gandomi, A. H. Alavi, "Krill herd: A new bio-inspired optimization algorithm", Communications in Nonlinear Science and Numerical Simulation, Vol. 17, No. 12, pp. 4831-4845, Dec. 2012 (doi:10.1016/j.cnsns. 2012.05.010).
[22] X. S. Yang, S. Deb, "Cuckoo search via Lévy flights". Proceedings of the IEEE/NaBIC, pp. 210-214, Coimbatore, India. 2009 (doi: 10.1109/NABIC.2009.5393690).
[23] X. S.Yang, "Firefly algorithms for multimodal optimization", International symposium on stochastic algorithms. Springer, Berlin, Heidelberg, Vol. 5792, pp. 169-178, 2009 (doi:10.1007/978-3-642-04944-6_14).
[24] S. Thawkar, R. Ingolikar, "Classification of masses in digital mammograms using firefly based optimization", International Journal of Image, Graphics & Signal Processing, Vol. 10, No. 2, Feb. 2018 (doi: 10.5815/ijigsp .2018.02.03).
[25] X. S. Yang, "Firefly algorithm, stochastic test functions and design optimisation", International Journal of Bio-Inspired Computation, Vol. 2, No. 2 , pp. 78-84, Mar. 2010.
[26] W. B. Sampaio, A. C. Silva, A. C. Paiva, M. Gattass, "Detection of masses in mammograms with adaption to breast density using genetic algorithm, phylogenetic trees, LBP and SVM", Expert Systems with Applications, Vol. 42, No. 22, pp. 8911-8928, Dec. 2015 (doi:10.1016/j.eswa.2015.07.046).
[27] S. W. Borges, E.M. Diniz, A. C. Silva, A. C. Paiva, M. Gattass, "Detection of masses in mammogram images using CNN, geostatistic functions and SVM", Computers in Biology and Medicine Vol.41, No.8, pp. 653-664, 2011 (doi:10.1016/j.compbiomed.2011.05.017).
[28] A. P. Nunes, , A. C. Silva, A. C. Paiva, "Detection of masses in mammographic images using simpson’s diversity index in circular regions and svm", International Journal of Signal and Imaging Systems Engineering, Vol. 3, No. 1, pp. 40-51, 2010 (doi:10.1007/978-3-642-03070-3_41).
[29] A. Keshavarz, H. Pourghasem, S. M. S. Ebrahimi, "Segmentation of melanoma and other pigmented skin lesions in dermoscopic images using fusion of threshoding methods based on reinforcement algorithm", Journal of Intelligent Procedures in Electrical Technology, Vol. 4, No. 16, pp. 37-48, Winter 2014.
[30] H. R. Javadi , H. Pourghasem, "kin lesion classification from dermoscopy images using color and shape features", Journal of Intelligent Procedures in Electrical Technology, Vol. 8, No. 29, pp. 33-40, Spring 2017.
