Abstract
This chapter is a typical example of usage of Computational Intelligence Techniques-CI-Techniques (Machine Learning-Artificial Intelligence) in medical data analysis problems, such as optimizing the Pap-Smear or Pap-Test diagnosis. Pap-Smear or Pap-Test is a method for diagnosing Cervical Cancer (4th leading cause of female cancer and 2nd common female cancer in the women aged 14–44 years old), invented by Dr. George Papanicolaou in 1928 (Bruni et al. in Human papillomavirus and related diseases in the world [1]; Marinakis and Dounias in The Pap Smear Benchmark, Intelligent and Nature Inspired Approaches in Pap Smear Diagnosis, Special Session Proceedings of the NISIS—2006 Symposium [2]). According to Pap-Smear, specialized doctors collect a sample of cells from specific areas of cervical, observe (using microscope) specific cells of the above cell-sample and classify these cells into 2 general (Normal and Abnormal cells) and 7 individual categories/classes: Superficial squamous epithelial, Intermediate squamous epithelial, Columnar epithelial, Mild squamous non-keratinizing dysplasia, Moderate squamous non-keratinizing dysplasia, Severe squamous non-keratinizing dysplasia and Squamous cell carcinoma in situ intermediate. The ideal aim of this classification process is the early diagnosis of cervical cancer. Pap-Test was a time-consuming process and with considerable errors of observation, resulting in diagnosis with a high degree of uncertainty. Considering these problems, Data Analysis researchers in collaboration with specialized doctors have presented several successful approaches to the Pap-Smear diagnosis optimization problem using Computational Intelligence (CI) Techniques, whose results are acceptable to the medical community and have room for improvement. An equivalent effort was made by the researchers and students of Department of Automation of the Technical University of Denmark for the first time in 1999 is the cornerstone of further Pap-Smear data analysis using CI-Techniques, which was then continued until nowadays by researchers and students of the Management and Decision Engineering Laboratory (MDE-Lab) of Technical Department of Financial and Management Engineering of the University of the Aegean (http://mde-lab.aegean.gr/downloads). This research focuses on the approach of the Pap-Smear Classification Problem with the use of CI-Techniques and has as an ideal goal the contribution of Artificial Intelligence to the optimization of medical diagnoses. In addition, the research conducted aims at diagnosing cervical cancer both at an early stage and at an advanced stage, improving the Pap-Smear classification process as well as the process of Feature Selection. The aforementioned research was based on two databases, called Old Data and New Data which consist of 500 and 917 single cell patterns respectively, described by 20 features. These data were collected by qualified doctors and cyto-technicians from the Department of Pathology of the Herlev University Hospital and are available at the web-page of MDE-Lab (http://mde-lab.aegean.gr/downloads). The CI-Techniques, that were used to build classifiers, both for the 2-class classification problem and for the 7-class classification problem, are machine learning algorithms, such as Adaptive Network-based Fuzzy Inference Systems, Artificial Neural Networks, k-Nearest Neighbor approaches, etc. Also, various algorithms, such as Fuzzy C-Means, Tabu Search, Ant Colony etc., were used to improve the operating processes, such as the training process or the feature selection approach. All these CI-Techniques are briefly presented in Sect. 3. Finally, the results of the application of the above CI-Techniques are presented in Sect. 4, and prove very satisfactory, fully accepted by the doctors of this particular field of medicine and with room for further improvement.
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References
L. Bruni, L. Barrionuevo-Rosas, G. Albero, B. Serrano, M. Mena, D. Gómez, J. Muñoz, F.X. Bosch, S. de Sanjosé, Human papillomavirus and related diseases in the world, Summary Report 27 July 2017, ICO/IARC Information Centre on HPV and Cancer (HPV Information Centre) (2017). http://www.hpvcentre.net/statistics/reports/XWX.pdf
Y. Marinakis, G. Dounias, Pap smear diagnosis using a hybrid intelligent scheme focusing on genetic algorithm based feature selection and nearest neighborhood classification, in The Pap Smear Benchmark, Intelligent and Nature Inspired Approaches in Pap Smear Diagnosis, Special Session Proceedings of the NISIS—2006 Symposium, 15–24, November 29–December 1 2006, Puerto de la Cruz, Tenerife, Spain (Spain, 2006), pp. 15–24
E. Rakus-Andersson, L.C. Jain, Computational intelligence in medical decisions making, in Recent Advances in Decision Making. Studies in Computational Intelligence, vol. 222 (Springer, Berlin, Heidelberg, 2009), pp. 145–159. https://doi.org/10.1007/978-3-642-02187-9_9
F. Lemke, J.-A. Müller, Medical data analysis using self-organizing data mining technologies, in Systems Analysis Modelling Simulation, vol. 43 (Taylor & Francis Group, London, 2010), pp. 1399–1408. https://doi.org/10.1080/02329290290027337
A. Tsanas, M.A. Little, P.E. McSharry, A methodology for the analysis of medical data, in A Methodology for the Analysis of Medical Data. Handbook of Systems and Complexity in Health, vol. 1 (Springer, New York, 2013), pp. 113–125. https://doi.org/10.1007/978-1-4614-4998-0_7
E. López-Rubio, D.A. Elizondo, M. Grootveld, J.M. Jerez, R.M. Luque-Baena, Computational intelligence techniques in medicine, in Computational and Mathematical Methods in Medicine, vol. 2015 (Hindawi, 2015), pp. 37–47. http://dx.doi.org/10.1155/2015/196976
A.N. Ramesh, C. Kambhampati, J.R.T. Monson, P.J. Drew, Artificial intelligence in medicine, in Annals of the Royal College of Surgeons of England, vol. 86 (PMC, 2004), pp. 334–338. http://doi.org/10.1308/147870804290
C.K. Reddy, C.C. Aggarwal, Healthcare Data Analytics, 1st edn. (CRC Press, Taylor & Francis Group, 2015). ISBN 978-1-4822-3211-0
R.A. Weinberg, The Biology of Cancer, 2nd edn. (Taylor & Francis Group, Garland Science, 2014). ISBN 978-0-8153-4219-9
A. González Martín, Molecular biology of cervical cancer, in Clinical and Translational Oncology, vol. 9 (Springer, Milan, 2007), pp. 347–354. https://doi.org/10.1007/s12094-007-0066-8
J.G. De la Garza-salazar, F. Morales-Vasquez, A. Meneses-García, Cervical Cancer (Springer, Switzerland, 2017). https://doi.org/10.1007/978-3-319-45231-9
J. Mothoneos, Understanding cervical cancer, a guide for women with cancer, their families and friends, in Cancer Council Australia Cancer Council SA, vol. 13 (Cancer Council Australia, 2017). ISBN 978-1-925651-03-4
R. Sankaranarayanan, J.W. Sellors, Colposcopy and treatment of cervical intraepithelial neoplasia, World Health Organization—International Agency for Research on Cancer (IARC), Lyon (2003). ISBN 9283204123
V. Mehta, V. Vasanth, C. Balachandran, Pap smear, in Indian Journal of Dermatology, Venereology and Leprology, vol. 75 (Wolters Kluwer Medknow Publications, 2009), pp. 214–216. https://doi.org/10.4103/0378-6323.48686
P. Pisani, R.J. Black, P. Pisani, M.T. Valdivieso, A.B. Miller, N.E. Day, M. Kallio, A.B. Miller, N.E. Day, H. Moller, P. Lauriola, E. Magliola, L. Bonelli, E. Rossi, C. Gustavino, M. Ferreri, M.R. Giovagnoli, C. Midulla, M.E. Boon, S. Beck, J.A. Knottnerus, N. Day, G. Douglas, E. Farney, E. Lynge, J. Philip, G.P. Vooijs, M. Confortini, A. Biggeri, A. Russo, The pap test process. Leonardo Da Vinci Project—Cytotrain (2000). http://www.apof.eu/ZAMBIA2/RobertoL/THE%20PAP%20TEST%20PROCESS.pdf
J. Byriel, Neuro-fuzzy classification of cells in cervical smears. MSc. thesis, Department of Automation, Technical University of Denmark. Lyngby, Denmark, 1999
A. Tsakonas, G. Dounias, J. Jantzen, B. Bjerregaard, A hybrid CI approach combining genetic programming and heuristic classification for Pap-Smear diagnosis, in Presented in “Hybrid CI Methods in Medicine” session, EUNITE-01, Tenerife, Spain, December 13–14, 2001, also published in G. Dounias, D.A. Linkens (eds.), Adaptive Systems and Hybrid Computational Intelligence in Medicine, pp. 123–132. Joint Publication of the University of the Aegean and EUNITE, The European Network on Intelligent Technologies for Smart Adaptive Systems (2001). ISBN 960-7475-19-4
E. Martin, Pap-Smear classification. MSc. thesis, Department of Automation, Technical University of Denmark. Lyngby, Denmark, 2003
N. Ampazis, G. Dounias, J. Jantzen, Pap-Smear classification using efficient second order neural network training algorithms, in Methods and Applications of Artificial Intelligence. SETN 2004. Lecture Notes in Computer Science, vol. 3025 (Springer, Berlin, Heidelberg, 2004), pp. 230–245. https://doi.org/10.1007/978-3-540-24674-9_25
J. Norap, Classification of Pap-Smear data by transudative neuro-fuzzy methods. MSc thesis, Department of Automation, Technical University of Denmark. Lyngby, Denmark, 2005
G. Dounias, B. Bjerregaard, J. Jantzen, A. Tsakonas, N. Ampazis, G. Panagi, E. Panourgias, Automated identification of cancerous smears using various competitive intelligent techniques, in Oncology Reports, vol. 15 (2006), pp. 1001–1006. https://doi.org/10.3892/or.15.4.1001
Y. Marinakis, G. Dounias, Nearest neighborhood based pap smear cell classification using tabu search for feature selection, in The Pap Smear Benchmark, Intelligent and Nature Inspired Approaches in Pap Smear Diagnosis, Special Session Proceedings of the NISIS—2006 Symposium, 25–34, November 29–December 1 2006, Puerto de la Cruz, Tenerife, Spain (Spain, 2006), pp. 25–34
Y. Marinakis, G. Dounias, Nature inspired intelligent techniques for pap smear diagnosis: ant colony optimization for cell classification, in The Pap Smear Benchmark, Intelligent and Nature Inspired Approaches in Pap Smear Diagnosis, Special Session Proceedings of the NISIS—2006 Symposium, 35–45, November 29–December 1, 2006, Puerto de la Cruz, Tenerife, Spain (Spain, 2006), pp. 35-45
J. Jantzen, G. Dounias, Analysis of Pap-Smear data, in NISIS 2006, The Pap Smear Benchmark, Intelligent and Nature Inspired Approaches in Pap Smear Diagnosis, Special Session Proceedings of the NISIS—2006 Symposium, 25–34, November 29–December 1 2006, Puerto de la Cruz, Tenerife, Spain (Spain, 2006), pp. 5–14
Y. Marinakis, M. Marinaki, G. Dounias, C. Zopounidis, Metaheuristic algorithms in medicine, the Pap-Smear cell classification problem, Book of Abstracts of the ECO-Q Management and Quality in Health Care, 30–31 March 2007, Chania, Greece (presentation) (Greece, 2007)
F. Glower, Tabu search-part I. ORSA J Comput. 3, 190–206 (1989). https://pubsonline.informs.org/doi/abs/10.1287/ijoc.1.3.190
J. Jang, ANFIS: adaptive-network-based fuzzy inference system, in IEEE Systems, Man, and Cybernetics Society, vol. 23 (IEE, 1993), pp. 665–685. https://doi.org/10.1109/21.256541
S. Haykin, Neural Networks and Learning Machines, 3rd edn. (Pearson Education, Inc. Upper Saddle River, New Jersey, 2009). ISBN 978-0-13-147139-9
F. Puppe, Heuristic classification, in Systematic Introduction to Expert Systems (Springer, Berlin, Heidelberg, 1993), pp. 131–148. https://doi.org/10.1007/978-3-642-77971-8_15
B.N. Prasad, M. Rathore, G. Gupta, T. Singh, Performance measure of hard c-means, fuzzy c-means and alternative c-means algorithms, in International Journal of Computer Science and Information Technologies (IJCSIT), vol. 7 (2016), pp. 878–883. http://ijcsit.com/docs/Volume%207/vol7issue2/ijcsit2016070297.pdf
M. Velikova, P.J.F. Lucas, N. Ferreira, M. Samulski, N. Karssemeijer, A decision support system for breast cancer detection in screening programs, in Proceedings of the 18th European Conference on Artificial Intelligence, ECAI 2008, vol. 178 (IOS Press, Amsterdam, 2008), pp. 658–662. http://doi.org/10.3233/978-1-58603-891-5-658
P. Goel, H. Liu, D. Brown, A. Datta, Spiking neural network based classification of task-evoked EEG signals, in Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science, vol. 4251 (Springer, Berlin, Heidelberg, 2006), pp. 825–832. https://doi.org/10.1007/11892960_99
K. Kabassi, M. Virvoua, G.A. Tsihrintzis, Y. Vlachos, D. Perrea, Specifying the personalization reasoning mechanism for an intelligent medical e-learning system on atheromatosis: an empirical study, in Intelligent Decision Technologies, vol. 2 (IOS Press, 2008), pp. 179–190. https://doi.org/10.3233/IDT-2008-2304
H. Kostakis, B. Boutsinas, D.B. Panagiotakos, L.D. Kounis, A computational algorithm for the risk assessment of developing acute coronary syndromes, using online analytical process methodology, in International Journal of Knowledge Engineering and Soft Data Paradigms, vol. 1 (Inderscience Enterprises Ltd, 2008), pp. 85–99. https://doi.org/10.1504/IJKESDP.2009.021986
F. Menolascina, R.T. Alves, S. Tommasi, P. Chiarappa, M. Delgado, V. Bevilacqua, G. Mastronardi, A.A. Freitas, A. Paradiso, Fuzzy rule induction and artificial immune systems in female breast cancer familiarity profiling, in Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science, vol. 4694 (Springer, Berlin, Heidelberg, 2007), pp. 830–837. https://doi.org/10.1007/978-3-540-74829-8_101
V.S. Kodogiannis, J.N. Lygouras, T. Pachidis, An intelligent decision support system in wireless-capsule endoscopy, in Intelligent Techniques and Tools for Novel System Architectures. Studies in Computational Intelligence, vol. 109 (Springer, Berlin, Heidelberg, 2008), pp. 259–275. https://doi.org/10.1007/978-3-540-77623-9_15
E. Kang, Y. Im, U. Kim, Remote control multi-agent system for u-healthcare service, in Agent and Multi-Agent Systems: Technologies and Applications. KES-AMSTA 2007. Lecture Notes in Computer Science, vol. 4496 (Springer, Berlin, Heidelberg, 2007), pp. 36–644. https://doi.org/10.1007/978-3-540-72830-6_66
C.W. Jeong, D.H. Kim, S.C. Joo, Mobile collaboration framework for u-healthcare agent services and its application using PDAs, in Agent and Multi-Agent Systems: Technologies and Applications. KES-AMSTA 2007. Lecture Notes in Computer Science, vol. 4496 (Springer, Berlin, Heidelberg, 2007), pp. 747–756. https://doi.org/10.1007/978-3-540-72830-6_78
R.M.A. Mateo, L.F. Cervantes, H.K. Yang, J. Lee, Mobile agents using data mining for diagnosis support in ubiquitous healthcare, in Agent and Multi-Agent Systems: Technologies and Applications. KES-AMSTA 2007. Lecture Notes in Computer Science, vol. 4496 (Springer, Berlin, Heidelberg, 2007), pp. 795–804. https://doi.org/10.1007/978-3-540-72830-6_83
M. Tentori, J. Favela, M. Rodriguez, Privacy-aware autonomous agents for pervasive healthcare, IEEE Intelligent Systems, vol. 21 (IEEE, 2006), pp. 55–62. https://doi.org/10.1109/MIS.2006.118
S.G. Nejad, R. Martens, R. Paranjape, An agent-based diabetic patient simulation, in Agent and Multi-Agent Systems: Technologies and Applications. KES-AMSTA 2008. Lecture Notes in Computer Science, vol. 4953 (Springer, Berlin, Heidelberg, 2008), pp. 832–841. https://doi.org/10.1007/978-3-540-78582-8_84
J. Koleszynska, GIGISim—the intelligent telehealth system: computer aided diabetes management—a new review, in Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science, vol. 4692 (Springer, Berlin, Heidelberg, 2007), pp. 789–796. https://doi.org/10.1007/978-3-540-74819-9_97
C. Koutsojannis, I. Hatzilygeroudis, Fuzzy-evolutionary synergism in an intelligent medical diagnosis system, in Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science, vol. 4252 (Springer, Berlin, Heidelberg, 2006), pp. 1313–1322. https://doi.org/10.1007/11893004_166
E. Papageorgiou, G. Georgoulas, C. Stylios, G. Nikiforidis, P. Groumpos, Combining fuzzy cognitive maps with support vector machines for bladder tumor grading. in Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science, vol. 4251 (Springer, Berlin, Heidelberg, 2006), pp. 515–523. https://doi.org/10.1007/11892960_63
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Karampotsis, E., Dounias, G., Jantzen, J. (2019). Machine Learning Approaches for Pap-Smear Diagnosis: An Overview. In: Tsihrintzis, G., Virvou, M., Sakkopoulos, E., Jain, L. (eds) Machine Learning Paradigms. Learning and Analytics in Intelligent Systems, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-030-15628-2_4
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