Research Article
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Diagnosing Hematological Disorders Using Deep Learning Method

Year 2021, Volume: 4 Issue: 2, 227 - 243, 31.08.2021

Tuba Karagül , Nilüfer Yurtay , Birgül Öneç

https://doi.org/10.35377/saucis.04.02.836375

Abstract

Deciding on the diagnosis of the disease is an important step for treating the patients. Also, the numerical value of blood tests, the personal information of patients, and most importantly, an expert opinion is necessary to diagnose a disease. With the development of technology, patient-related data are obtained both rapidly and in large sizes. Deep learning methods, which can produce meaningful results by processing the data in raw form, are beginning to give results that are close to human opinion nowadays. The present work is aimed to develop a system that will enable the diagnosis of anemia in general practice conditions due to the increasing number of patients and the intention of the hospitals, as well as the difficulties in reaching the expert medical consultant. The main contribution of this work is to make a diagnosis like a doctor with the data as the way the doctor uses it. The data set was obtained from the actual hospital environment and no intervention, such as increasing or decreasing the number of data, increasing or decreasing the number of attributes, reduction, integration, imputation, transformation, or discretization, has been made on the incoming patient data. The original hospital data are classified for the diagnosis of anemia types and the accuracy of 84,97% achieved by using a deep learning algorithm.

Keywords

deep learning anemia classification

Supporting Institution

Research Fund of Sakarya University, Turkey

Project Number

2015-50-02-010

References

  • T.Vos, D. Flaxman et al., Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990–2010: A systematic analysis for the Global Burden of Disease Study 2010, The Lancet, 380, 2163-2196, DOI: 10.1016/S0140-6736;12;61729-2, 2012.
  • Hemoglobin concentrations for the diagnosis of anemia and assessment of severity Vitamin and Mineral Nutrition Information System. Geneva, World Health Organization, Available: http://www.who.int/vmnis/indicators/haemoglobin.pdf (2011).
  • N.J. Kiassebaum, R. Jasrasaria et al., A systematic analysis of global anemia burden from 1990 to 2010. Blood, 123, 615-624, DOI: 10.1182/blood-2013-06-508325, 2014.
  • J.R. Beck, J.R. Bell, F.Hirai, J.J. Simmons, H.C. Jr. Lyon, Computer-Based Exercises in Cardiac Diagnosis (PlanAlyzer), Proc. Annu. Symp. Computer Applications in Medical Care, Nov 9, 403-408, PMCID: PMC2245328, 1988.
  • J.R. Beck, J.F. O’Donnell, F. Hirai, J.J. Simmons, J.C. Healy, H. C. Jr. Lyon, Computer-based exercises in anemia diagnosis (PlanAlyzer), Methods Inf. Med., 28:4, 364-369, PMID:2695787, 1989.
  • H. C. Jr. Lyon, J.R. Bell, J.F. O’Donnell, F. Hirai, J.C. Healy, J.R. Beck, The PlanAlyzer Cases for Teaching Clinical Reasoning: A Demonstration of the Cases, Discussion of the Research & Development Process, Lessons Learned and Strategies for Introducing Computer-Based Programs into Medical School Courses as a Vehicle for Curriculum Reform, Proc. Annu. Symp. Computer Applications in Medical Care, PMCID: PMC2850769, 1993.
  • M. Lipkin, Correlation of Data with a Digital Computer in the Differential Diagnosis of Hematological Diseases, IRE Transactions on Medical electronics, 243-246, 1960.
  • R. L. Engle, B. J. Flehinger, S. Allen, R. Friedman, M. Lipkin, B. J. Davis, L. L. Leveridge, HEME: A Computer Aid to Diagnosis of Hematologic Disease, Bulletin of the New York Academy of Medicine, 52:5, 584–600, 1976.
  • I. Azarkhish, M.R. Raoufy, S. Gharibzadeh, Artificial Intelligence Models for Predicting Iron Deficiency Anemia and Iron Serum Level Based on Accessible Laboratory Data, J Med Syst, 36, 2057-2061, DOI: 10.1007/s10916-011-9668-3, 2012.
  • Z. Yılmaz, M.R. Bozkurt, Determination of Women Iron Deficiency Anemia Using Neural Networks, J Med Syst, 36, 2941-2945, DOI: 10.1007/s10916-011-9772-4, 2012.
  • A. Yılmaz, M. Dağlı, N. Allahverdi, A Fuzzy Expert System Design for Iron Deficiency Anemia, IEEE 7th International Conference on Application of Information and Communication Technologies (AICT), Baku, Azerbaijan, DOI: 10.1109/ICAICT.2013.6722707, 2013.
  • W. Reinisch, M. Staun, S Bhandari., M. Munoz, State of the iron: How to diagnose and efficiently treat iron deficiency anemia in inflammatory bowel disease, Journal of Crohn’s and Colitis, 7, 429-440, 2013.
  • Ş. Doğan, I. Türkoğlu, Iron-Deficiency Anemia Detection from Hematology Parameters by Using Decision Trees, International Journal of Science & Technology, 3:1, 85-92, 2008.
  • B.C. Yavuz, T. Karagül Yıldız, N. Yurtay, Z.Yılmaz, Comparison of K Nearest Neighbors and Regression Tree Classifiers Used with Clonal Selection Algorithm to Diagnose Hematological Diseases, AJIT-e: Online Academic Journal of Information Technology, 5-16, DOI: 10.5824/1309-1581.2014.3.001. x, 2014.
  • S. A. Sanap, M. Nagori, V. Kshirsagar, Classification of Anemia Using Data Mining Techniques, Presented in SEMCCO 2011, Berlin Heidelberg, Part II, 2011.
  • N. Amin, A. Habib, Comparison of Different Classification Techniques Using WEKA for Hematological Data, Am J Eng Res, 4:3, 55-61, e-ISSN: 2320- 0847, 2015.
  • I. Yılmaz, Demir eksikliği anemisi ile beta talasemi minörün ayırıcı tanısında eritrosit indekslerin rolü, Expertise Thesis, Dept. of Internal Medicine, Faculty of Medicine, Pamukkale University, Denizli, Turkey, 2010.
  • E. Urrechaga, U. Aguirre, S. Izquierdo, Differential Diagnosis of Microcytic Anemia, Anemia, DOI: 10.1155/2013/457834, 2013.
  • M. K. Jamei, K. M. Talarposhti, Discrimination between Iron Deficiency Anaemia (IDA) and β - Thalassemia Trait (β-TT) Based on Pattern-Based Input Selection Artificial Neural Network (PBIS- ANN), J Adv Comp Res, 7: 4, 55-66, pISSN: 2345-606x, eISSN: 2345-6078, 2016.
  • R. Kishore, K.P. Rao, G.R.S. Murthy, Performance Evaluation of Entropy and Gini using Threaded and Non-Threaded ID3 on Anaemia Dataset, Presented at Fifth International Conference on Communication Systems and Network Technologies, IEEE, 2015.
  • M. F. Shaik, M. Subashini, Anemia Diagnosis by Fuzzy Logic Using LabVIEW, Presented at IEEE International Conference on Intelligent Computing and Control (I2C2), DOI: 10.1109/I2C2.2017.8321790, 2017.
  • P. T. Dalvi, N. Vernekar, Anemia Detection using Ensemble Learning Techniques and Statistical Models, Presented at IEEE International Conference on Recent Trends in Electronics Information Communication Technology, May 20-21, 2016.
  • S. Belginova, I. Uvaliyeva, A. Ismukhamedova, Decision Support System for Diagnosing Anemia, Presented at 4th International Conference on Computer and Technology Applications, DOI: 10.1109/CATA.2018.8398684, 2018.
  • G. Dimauro, D. Caivano, F. Girardi, A New Method and a Non-Invasive Device to Estimate Anemia Based on Digital Images of the Conjunctiva, IEEE Access-Special Section on Human-Centered Smart Systems and Technologies, DOI: 10.1109/ACCESS.2018.2867110, 2018.
  • M. Hasani, A. Hanani, Automated Diagnosis of Iron Deficiency Anemia and Thalassemia by Data Mining Techniques, International Journal of Computer Science and Network Security, 17:4, 326-331, 2017.
  • G. Gunčar, M. Kukar, M. Notar, M. Brvar, P. Černelč, M. Notar, M. Notar, An application of machine learning to haematological diagnosis, Scientific Reports, 8:411, 1-12, DOI:10.1038/s41598-017-18564-8, 2018.
  • H. Ayyıldız, S. A. Tuncer, Determination of the effect of red blood cell parameters in the discrimination of iron deficiency anemia and beta thalassemia via Neighborhood Component Analysis Feature Selection-Based machine learning, Chemometrics and Intelligent Laboratory Systems, 196, 1-8, DOI: https://doi.org/10.1016/j.chemolab.2019.103886, 2020.
  • J. Schmidhuber, Deep learning in neural networks: An overview, Neural Networks, 61, 85–117. DOI: 10.1016/j.neunet.2014.09.003, 2015.
  • W. Liu, Z. Wang, X. Liu, N. Zeng, Y. Liu, F. E. Alsaadi, A survey of deep neural network architecture and their applications, Neurocomputing, 234, 11–26, DOI: 10.1016/j.neucom.2016.12.038, 2017.
  • G. Litjens et al., A survey on deep learning in medical image analysis, Medical Image Analysis, 42, 60–88, DOI: 10.1016/j.media.2017.07.005, 2017.
  • R. Miotto, L. Li, B. A. Kidd, J. T. Dudley, Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records, Scientific Reports, 6:26094,1-10. DOI: 10.1038/srep26094, 2016.
  • W. Yu, J. Chang, C. Yang, L. Zhang, H. Shen, Y. Xia, J. Sha, Automatic Classification of Leukocytes Using Deep Neural Network, IEEE 12th International Conference on ASIC, DOI: 10.1109/ASICON.2017.8252657, 2017.
  • M. Xu, D.P. Papageorgiou, S.Z. Abidi, M. Dao, H. Zhao, G.E. Karniadakis, A deep convolutional neural network for classification of red blood cells in sickle cell anemia, PLoS Computational Biology, 13:10, DOI: https://doi.org/10.1371/journal.pcbi.1005746, 2017.
  • K. Kimura, Y. Tabe, T. Ai, I. Takehara, H. Fukuda, H. Takahashi, T. Naito, N. Komatsu, K. Uchihashi, A. Ohsaka, A novel automated image analysis system using deep convolutional neural networks can assist to differentiate MDS and AA, Scientific Reports, 9, 1-9, DOI: https://doi.org/10.1038/s41598-019-49942-z, 2019.
  • F. K. Alsheref, W. H. Gomaa, Blood Diseases Detection using Classical Machine Learning Algorithms, International Journal of Advanced Computer Science and Applications, 10:7, 77-81, 2019.
  • B. Çil, H. Ayyıldız, T. Tuncer, Discrimination of β-thalassemia and iron deficiency anemia through extreme learning machine and regularized extreme learning machine-based decision support system, Medical Hypotheses, 138, 1-6, DOI: https://doi.org/10.1016/j.mehy.2020.109611, 2020.
  • N. Varghese, Machine Learning Techniques for the Classification of Blood Cells and Prediction of Diseases, International Journal of Computer Science Engineering, 9:1, 66-75, 2020.
  • L. Alzubaidi, M. A. Fadhel, O. Al‐Shamma, J. Zhang, Y. Duan, Deep Learning Models for Classification of Red Blood Cells in Microscopy Images to Aid in Sickle Cell Anemia Diagnosis, Electronics, 9:427, 1-20, DOI:10.3390/electronics9030427, 2020.
  • S. Devunooru, A. Alsadoon, P. W. C. Chandana, A. Beg, Deep learning neural networks for medical image segmentation of brain tumours for diagnosis: a recent review and taxonomy, Journal of Ambient Intelligence and Humanized Computing (2021) 12:455–483, https://doi.org/10.1007/s12652-020-01998-w
  • E. Goceri, Deep learning based classification of facial dermatological disorders, Computers in Biology and Medicine, Volume 128, January 2021, 104118, https://doi.org/10.1016/j.compbiomed.2020.104118
  • S. KILICARSLAN, M. CELIK, Ş. SAHIN, Hybrid models based on genetic algorithm and deep learning algorithms for nutritional Anemia disease classification, Biomedical Signal Processing and Control, Volume 63, January 2021, 102231, https://doi.org/10.1016/j.bspc.2020.102231
  • S. Yeruva, M. S. Varalakshmi, B. P. Gowtham, Y. H. Chandana, PESN. K. Prasad, Identification of Sickle Cell Anemia Using Deep Neural Networks, Emerging Science Journal (ISSN: 2610-9182) Vol. 5, No. 2, April, 2021, http://dx.doi.org/10.28991/esj-2021-01270
  • A. Gupta, Anjum, S. Gupta, R. Katarya, InstaCovNet-19: A deep learning classification model for the detection of COVID-19 patients using Chest X-ray, Applied Soft Computing, Volume 99, February 2021, 106859, https://doi.org/10.1016/j.asoc.2020.106859
  • J. Venugopalan, L. Tong, H. R. Hassanzadeh, M. D. Wang, Multimodal deep learning models for early detection ofAlzheimer’s disease stage, Scientific Reports, (2021) vol. 11, article number. 3254, https://doi.org/10.1038/s41598-020-74399-w
  • Ü. Atila, M. Uçar, K. Akyol, E. Uçar, Plant leaf disease classification using EfficientNet deep learning model, Ecological Informatics, Volume 61, March 2021, 101182, https://doi.org/10.1016/j.ecoinf.2020.101182
  • Haematology in Clinical Practice, Part 1: Erythrocyte Disorders, Chapter 2. Clinical Approach to Anemia, 5th ed., Güneş Medical Bookstore, ISBN: 978-975-277-404-9, 2012.
  • B. Onec, Personal Lecture Notes of Birgül Öneç, Duzce University, Medical School, Department of Internal Medicine Sciences, 2017.
  • R. S. Hillman, K. A. Ault, M. Leporrier, H. M. Rinder, Haematology in Clinical Practice. Part 1: Red Blood Cell Disorders, Chapter 2. Clinical Approach to Anemia, 5th ed., Istanbul, Turkey: Güneş Medical Bookstore, ISBN: 978-975-277-404-9, 2012.
  • Guide to diagnosis and treatment of Erythrocyte Diseases and Hemoglobin disorders, Turkish Hematology Association, Version1-July2011, Available: www.thd.org.tr, 2011.
  • R. Hoffman, E.J. Benz, L.E. Silberstein, H.E. Heslop, Weitz J.I., Anastasi J., Hematology: Basic Principles and Practice, 6th edition, ISBN: 978-1-4377-2928-3, Elsevier, 2013.
  • A. E. Hassanien, E. T. Al-Shammari, N. Ghali, Computational Intelligence Techniques in Bioinformatics, Computational Biology and Chemistry, DOI: 10.1016/j.compbiolchem. 2013.04.007, 2013.
  • E. Öztemel, Artificial Neural Networks, Papatya Press, Istanbul, Turkey, 2006. ISBN: 975-67-97-39-8.
  • Rapidminer Tutuorial, 2017. [Online]. Available: https://docs.rapidminer.com/studio/operators/modeling/predictive/neural_nets/deep_learning.html [Accessed: 30.10.2017].
  • T. Fawcett, An introduction to ROC analysis, Pattern Recognition Letters, 27, 861-874, 2006.
  • F.Ramzan, M.U.G.Khan, A.Rehmat, S.Iqbal, T.Saba, A.Rehman, Z.Mehmood, A deep learning approach for automated diagnosis and multi-class classification of Alzheimer’s disease stages using resting-state fMRI and residual neural networks, Journal of Medical Systems vol.44(37), 2020.

Year 2021, Volume: 4 Issue: 2, 227 - 243, 31.08.2021

Tuba Karagül , Nilüfer Yurtay , Birgül Öneç

https://doi.org/10.35377/saucis.04.02.836375

Abstract

Project Number

2015-50-02-010

References

  • T.Vos, D. Flaxman et al., Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990–2010: A systematic analysis for the Global Burden of Disease Study 2010, The Lancet, 380, 2163-2196, DOI: 10.1016/S0140-6736;12;61729-2, 2012.
  • Hemoglobin concentrations for the diagnosis of anemia and assessment of severity Vitamin and Mineral Nutrition Information System. Geneva, World Health Organization, Available: http://www.who.int/vmnis/indicators/haemoglobin.pdf (2011).
  • N.J. Kiassebaum, R. Jasrasaria et al., A systematic analysis of global anemia burden from 1990 to 2010. Blood, 123, 615-624, DOI: 10.1182/blood-2013-06-508325, 2014.
  • J.R. Beck, J.R. Bell, F.Hirai, J.J. Simmons, H.C. Jr. Lyon, Computer-Based Exercises in Cardiac Diagnosis (PlanAlyzer), Proc. Annu. Symp. Computer Applications in Medical Care, Nov 9, 403-408, PMCID: PMC2245328, 1988.
  • J.R. Beck, J.F. O’Donnell, F. Hirai, J.J. Simmons, J.C. Healy, H. C. Jr. Lyon, Computer-based exercises in anemia diagnosis (PlanAlyzer), Methods Inf. Med., 28:4, 364-369, PMID:2695787, 1989.
  • H. C. Jr. Lyon, J.R. Bell, J.F. O’Donnell, F. Hirai, J.C. Healy, J.R. Beck, The PlanAlyzer Cases for Teaching Clinical Reasoning: A Demonstration of the Cases, Discussion of the Research & Development Process, Lessons Learned and Strategies for Introducing Computer-Based Programs into Medical School Courses as a Vehicle for Curriculum Reform, Proc. Annu. Symp. Computer Applications in Medical Care, PMCID: PMC2850769, 1993.
  • M. Lipkin, Correlation of Data with a Digital Computer in the Differential Diagnosis of Hematological Diseases, IRE Transactions on Medical electronics, 243-246, 1960.
  • R. L. Engle, B. J. Flehinger, S. Allen, R. Friedman, M. Lipkin, B. J. Davis, L. L. Leveridge, HEME: A Computer Aid to Diagnosis of Hematologic Disease, Bulletin of the New York Academy of Medicine, 52:5, 584–600, 1976.
  • I. Azarkhish, M.R. Raoufy, S. Gharibzadeh, Artificial Intelligence Models for Predicting Iron Deficiency Anemia and Iron Serum Level Based on Accessible Laboratory Data, J Med Syst, 36, 2057-2061, DOI: 10.1007/s10916-011-9668-3, 2012.
  • Z. Yılmaz, M.R. Bozkurt, Determination of Women Iron Deficiency Anemia Using Neural Networks, J Med Syst, 36, 2941-2945, DOI: 10.1007/s10916-011-9772-4, 2012.
  • A. Yılmaz, M. Dağlı, N. Allahverdi, A Fuzzy Expert System Design for Iron Deficiency Anemia, IEEE 7th International Conference on Application of Information and Communication Technologies (AICT), Baku, Azerbaijan, DOI: 10.1109/ICAICT.2013.6722707, 2013.
  • W. Reinisch, M. Staun, S Bhandari., M. Munoz, State of the iron: How to diagnose and efficiently treat iron deficiency anemia in inflammatory bowel disease, Journal of Crohn’s and Colitis, 7, 429-440, 2013.
  • Ş. Doğan, I. Türkoğlu, Iron-Deficiency Anemia Detection from Hematology Parameters by Using Decision Trees, International Journal of Science & Technology, 3:1, 85-92, 2008.
  • B.C. Yavuz, T. Karagül Yıldız, N. Yurtay, Z.Yılmaz, Comparison of K Nearest Neighbors and Regression Tree Classifiers Used with Clonal Selection Algorithm to Diagnose Hematological Diseases, AJIT-e: Online Academic Journal of Information Technology, 5-16, DOI: 10.5824/1309-1581.2014.3.001. x, 2014.
  • S. A. Sanap, M. Nagori, V. Kshirsagar, Classification of Anemia Using Data Mining Techniques, Presented in SEMCCO 2011, Berlin Heidelberg, Part II, 2011.
  • N. Amin, A. Habib, Comparison of Different Classification Techniques Using WEKA for Hematological Data, Am J Eng Res, 4:3, 55-61, e-ISSN: 2320- 0847, 2015.
  • I. Yılmaz, Demir eksikliği anemisi ile beta talasemi minörün ayırıcı tanısında eritrosit indekslerin rolü, Expertise Thesis, Dept. of Internal Medicine, Faculty of Medicine, Pamukkale University, Denizli, Turkey, 2010.
  • E. Urrechaga, U. Aguirre, S. Izquierdo, Differential Diagnosis of Microcytic Anemia, Anemia, DOI: 10.1155/2013/457834, 2013.
  • M. K. Jamei, K. M. Talarposhti, Discrimination between Iron Deficiency Anaemia (IDA) and β - Thalassemia Trait (β-TT) Based on Pattern-Based Input Selection Artificial Neural Network (PBIS- ANN), J Adv Comp Res, 7: 4, 55-66, pISSN: 2345-606x, eISSN: 2345-6078, 2016.
  • R. Kishore, K.P. Rao, G.R.S. Murthy, Performance Evaluation of Entropy and Gini using Threaded and Non-Threaded ID3 on Anaemia Dataset, Presented at Fifth International Conference on Communication Systems and Network Technologies, IEEE, 2015.
  • M. F. Shaik, M. Subashini, Anemia Diagnosis by Fuzzy Logic Using LabVIEW, Presented at IEEE International Conference on Intelligent Computing and Control (I2C2), DOI: 10.1109/I2C2.2017.8321790, 2017.
  • P. T. Dalvi, N. Vernekar, Anemia Detection using Ensemble Learning Techniques and Statistical Models, Presented at IEEE International Conference on Recent Trends in Electronics Information Communication Technology, May 20-21, 2016.
  • S. Belginova, I. Uvaliyeva, A. Ismukhamedova, Decision Support System for Diagnosing Anemia, Presented at 4th International Conference on Computer and Technology Applications, DOI: 10.1109/CATA.2018.8398684, 2018.
  • G. Dimauro, D. Caivano, F. Girardi, A New Method and a Non-Invasive Device to Estimate Anemia Based on Digital Images of the Conjunctiva, IEEE Access-Special Section on Human-Centered Smart Systems and Technologies, DOI: 10.1109/ACCESS.2018.2867110, 2018.
  • M. Hasani, A. Hanani, Automated Diagnosis of Iron Deficiency Anemia and Thalassemia by Data Mining Techniques, International Journal of Computer Science and Network Security, 17:4, 326-331, 2017.
  • G. Gunčar, M. Kukar, M. Notar, M. Brvar, P. Černelč, M. Notar, M. Notar, An application of machine learning to haematological diagnosis, Scientific Reports, 8:411, 1-12, DOI:10.1038/s41598-017-18564-8, 2018.
  • H. Ayyıldız, S. A. Tuncer, Determination of the effect of red blood cell parameters in the discrimination of iron deficiency anemia and beta thalassemia via Neighborhood Component Analysis Feature Selection-Based machine learning, Chemometrics and Intelligent Laboratory Systems, 196, 1-8, DOI: https://doi.org/10.1016/j.chemolab.2019.103886, 2020.
  • J. Schmidhuber, Deep learning in neural networks: An overview, Neural Networks, 61, 85–117. DOI: 10.1016/j.neunet.2014.09.003, 2015.
  • W. Liu, Z. Wang, X. Liu, N. Zeng, Y. Liu, F. E. Alsaadi, A survey of deep neural network architecture and their applications, Neurocomputing, 234, 11–26, DOI: 10.1016/j.neucom.2016.12.038, 2017.
  • G. Litjens et al., A survey on deep learning in medical image analysis, Medical Image Analysis, 42, 60–88, DOI: 10.1016/j.media.2017.07.005, 2017.
  • R. Miotto, L. Li, B. A. Kidd, J. T. Dudley, Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records, Scientific Reports, 6:26094,1-10. DOI: 10.1038/srep26094, 2016.
  • W. Yu, J. Chang, C. Yang, L. Zhang, H. Shen, Y. Xia, J. Sha, Automatic Classification of Leukocytes Using Deep Neural Network, IEEE 12th International Conference on ASIC, DOI: 10.1109/ASICON.2017.8252657, 2017.
  • M. Xu, D.P. Papageorgiou, S.Z. Abidi, M. Dao, H. Zhao, G.E. Karniadakis, A deep convolutional neural network for classification of red blood cells in sickle cell anemia, PLoS Computational Biology, 13:10, DOI: https://doi.org/10.1371/journal.pcbi.1005746, 2017.
  • K. Kimura, Y. Tabe, T. Ai, I. Takehara, H. Fukuda, H. Takahashi, T. Naito, N. Komatsu, K. Uchihashi, A. Ohsaka, A novel automated image analysis system using deep convolutional neural networks can assist to differentiate MDS and AA, Scientific Reports, 9, 1-9, DOI: https://doi.org/10.1038/s41598-019-49942-z, 2019.
  • F. K. Alsheref, W. H. Gomaa, Blood Diseases Detection using Classical Machine Learning Algorithms, International Journal of Advanced Computer Science and Applications, 10:7, 77-81, 2019.
  • B. Çil, H. Ayyıldız, T. Tuncer, Discrimination of β-thalassemia and iron deficiency anemia through extreme learning machine and regularized extreme learning machine-based decision support system, Medical Hypotheses, 138, 1-6, DOI: https://doi.org/10.1016/j.mehy.2020.109611, 2020.
  • N. Varghese, Machine Learning Techniques for the Classification of Blood Cells and Prediction of Diseases, International Journal of Computer Science Engineering, 9:1, 66-75, 2020.
  • L. Alzubaidi, M. A. Fadhel, O. Al‐Shamma, J. Zhang, Y. Duan, Deep Learning Models for Classification of Red Blood Cells in Microscopy Images to Aid in Sickle Cell Anemia Diagnosis, Electronics, 9:427, 1-20, DOI:10.3390/electronics9030427, 2020.
  • S. Devunooru, A. Alsadoon, P. W. C. Chandana, A. Beg, Deep learning neural networks for medical image segmentation of brain tumours for diagnosis: a recent review and taxonomy, Journal of Ambient Intelligence and Humanized Computing (2021) 12:455–483, https://doi.org/10.1007/s12652-020-01998-w
  • E. Goceri, Deep learning based classification of facial dermatological disorders, Computers in Biology and Medicine, Volume 128, January 2021, 104118, https://doi.org/10.1016/j.compbiomed.2020.104118
  • S. KILICARSLAN, M. CELIK, Ş. SAHIN, Hybrid models based on genetic algorithm and deep learning algorithms for nutritional Anemia disease classification, Biomedical Signal Processing and Control, Volume 63, January 2021, 102231, https://doi.org/10.1016/j.bspc.2020.102231
  • S. Yeruva, M. S. Varalakshmi, B. P. Gowtham, Y. H. Chandana, PESN. K. Prasad, Identification of Sickle Cell Anemia Using Deep Neural Networks, Emerging Science Journal (ISSN: 2610-9182) Vol. 5, No. 2, April, 2021, http://dx.doi.org/10.28991/esj-2021-01270
  • A. Gupta, Anjum, S. Gupta, R. Katarya, InstaCovNet-19: A deep learning classification model for the detection of COVID-19 patients using Chest X-ray, Applied Soft Computing, Volume 99, February 2021, 106859, https://doi.org/10.1016/j.asoc.2020.106859
  • J. Venugopalan, L. Tong, H. R. Hassanzadeh, M. D. Wang, Multimodal deep learning models for early detection ofAlzheimer’s disease stage, Scientific Reports, (2021) vol. 11, article number. 3254, https://doi.org/10.1038/s41598-020-74399-w
  • Ü. Atila, M. Uçar, K. Akyol, E. Uçar, Plant leaf disease classification using EfficientNet deep learning model, Ecological Informatics, Volume 61, March 2021, 101182, https://doi.org/10.1016/j.ecoinf.2020.101182
  • Haematology in Clinical Practice, Part 1: Erythrocyte Disorders, Chapter 2. Clinical Approach to Anemia, 5th ed., Güneş Medical Bookstore, ISBN: 978-975-277-404-9, 2012.
  • B. Onec, Personal Lecture Notes of Birgül Öneç, Duzce University, Medical School, Department of Internal Medicine Sciences, 2017.
  • R. S. Hillman, K. A. Ault, M. Leporrier, H. M. Rinder, Haematology in Clinical Practice. Part 1: Red Blood Cell Disorders, Chapter 2. Clinical Approach to Anemia, 5th ed., Istanbul, Turkey: Güneş Medical Bookstore, ISBN: 978-975-277-404-9, 2012.
  • Guide to diagnosis and treatment of Erythrocyte Diseases and Hemoglobin disorders, Turkish Hematology Association, Version1-July2011, Available: www.thd.org.tr, 2011.
  • R. Hoffman, E.J. Benz, L.E. Silberstein, H.E. Heslop, Weitz J.I., Anastasi J., Hematology: Basic Principles and Practice, 6th edition, ISBN: 978-1-4377-2928-3, Elsevier, 2013.
  • A. E. Hassanien, E. T. Al-Shammari, N. Ghali, Computational Intelligence Techniques in Bioinformatics, Computational Biology and Chemistry, DOI: 10.1016/j.compbiolchem. 2013.04.007, 2013.
  • E. Öztemel, Artificial Neural Networks, Papatya Press, Istanbul, Turkey, 2006. ISBN: 975-67-97-39-8.
  • Rapidminer Tutuorial, 2017. [Online]. Available: https://docs.rapidminer.com/studio/operators/modeling/predictive/neural_nets/deep_learning.html [Accessed: 30.10.2017].
  • T. Fawcett, An introduction to ROC analysis, Pattern Recognition Letters, 27, 861-874, 2006.
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Details

Primary Language English
Subjects Artificial Intelligence
Journal Section Articles
Authors

Tuba Karagül 0000-0002-9382-2606

Nilüfer Yurtay 0000-0002-7577-7506

Birgül Öneç 0000-0003-2824-1044

Project Number 2015-50-02-010
Publication Date August 31, 2021
Submission Date December 5, 2020
Acceptance Date July 19, 2021
Published in Issue Year 2021Volume: 4 Issue: 2

Cite

IEEE T. Karagül, N. Yurtay, and B. Öneç, “Diagnosing Hematological Disorders Using Deep Learning Method”, SAUCIS, vol. 4, no. 2, pp. 227–243, 2021, doi: 10.35377/saucis.04.02.836375.
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