Machine Learning in Detection and Classification of Leukemia Using Smear Blood Images: A Systematic Review

Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran Department of Hematology and Blood Banking, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran Pediatric Congenital Hematologic Disorders Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran Department of Computer Science, Sama Technical and Vocational Training College, Tehran Branch (Tehran), Islamic Azad University (IAU), Tehran, Iran

[1]  Seifedine Kadry,et al.  Recognition of Different Types of Leukocytes Using YOLOv2 and Optimized Bag-of-Features , 2020, IEEE Access.

[2]  Bin Zhu,et al.  Blood cell identification using neural networks , 1996, Proceedings of the IEEE 22nd Annual Northeast Bioengineering Conference.

[3]  Z. Obermeyer,et al.  Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. , 2016, The New England journal of medicine.

[4]  S. Namayandeh,et al.  GLOBAL Leukemia in Children 0-14 Statistics 2018, Incidence and Mortality and Human Development Index (HDI): GLOBOCAN Sources and Methods , 2020, Asian Pacific journal of cancer prevention : APJCP.

[5]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[6]  J. Hari,et al.  Separation and counting of blood cells using geometrical features and distance transformed watershed , 2014, 2014 2nd International Conference on Devices, Circuits and Systems (ICDCS).

[7]  J W Arends,et al.  Efforts to improve interobserver agreement in histopathological grading. , 1995, Journal of clinical epidemiology.

[8]  Archana B. Patankar,et al.  Segmentation of Blast using Vector Quantization Technique , 2013 .

[9]  Antonis A. Argyros,et al.  Efficient model-based 3D tracking of hand articulations using Kinect , 2011, BMVC.

[10]  Mustafa Ghaderzadeh,et al.  Comparing Performance of Different Neural Networks for Early Detection of Cancer from Benign Hyperplasia of Prostate , 2013 .

[11]  Keerthana Prasad,et al.  Automated Decision Support System for Detection of Leukemia from Peripheral Blood Smear Images , 2019, Journal of Digital Imaging.

[12]  M. Y. Mashor,et al.  Classification of acute leukaemia cells using multilayer perceptron and simplified fuzzy ARTMAP neural networks , 2013, Int. Arab J. Inf. Technol..

[13]  Lyle J. Palmer,et al.  Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework , 2017, Scientific Reports.

[14]  H. S. Bhadauria,et al.  Computer Aided Diagnostic System for Detection of Leukemia Using Microscopic Images , 2015 .

[15]  Dipti Patra,et al.  Lymphocyte image segmentation using functional link neural architecture for acute leukemia detection , 2012, Biomedical Engineering Letters.

[16]  Banshidhar Majhi,et al.  Gray level co-occurrence matrix and random forest based acute lymphoblastic leukemia detection , 2017, Biomed. Signal Process. Control..

[17]  Antoine Manzanera,et al.  Automatic Analysis of Microscopic Images in Hematological Cytology Applications , 2011 .

[18]  Yo-Sung Ho,et al.  Automatic Cell Classification in Human's Peripheral Blood Images Based on Morphological Image Processing , 2001, Australian Joint Conference on Artificial Intelligence.

[19]  Sven Koitka,et al.  Traditional Feature Engineering and Deep Learning Approaches at Medical Classification Task of ImageCLEF 2016 , 2016, CLEF.

[20]  Keerthana Prasad,et al.  Development of a Robust Algorithm for Detection of Nuclei and Classification of White Blood Cells in Peripheral Blood Smear Images , 2018, Journal of Medical Systems.

[21]  Ashutosh Mishra,et al.  Automated Leukaemia Detection Using Microscopic Images , 2015 .

[22]  S. Allwin,et al.  Analysis for textural features in Nuclei of Cervical Cyto Images , 2010, 2010 IEEE International Conference on Computational Intelligence and Computing Research.

[23]  Jitendra Virmani,et al.  Computer assisted classification framework for prediction of acute lymphoblastic and acute myeloblastic leukemia , 2017 .

[24]  Jianwei Zhao,et al.  Automatic detection and classification of leukocytes using convolutional neural networks , 2016, Medical & Biological Engineering & Computing.

[25]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[26]  Lalit Mohan Saini,et al.  Robust technique for the detection of Acute Lymphoblastic Leukemia , 2015, 2015 IEEE Power, Communication and Information Technology Conference (PCITC).

[27]  F E Al-Tahhan,et al.  Accurate automatic detection of acute lymphatic leukemia using a refined simple classification , 2020, Microscopy research and technique.

[28]  Nidan Qiao.  A systematic review on machine learning in sellar region diseases: quality and reporting items , 2019, Endocrine connections.

[29]  L Zhang,et al.  Processing Technology in Microscopic Images of Cancer Cells in Pleural Fluid Based on Fuzzy Edge Detection Method , 2006 .

[30]  Nilam Nur Amir Sjarif,et al.  Acute lymphoblastic leukemia segmentation using local pixel information , 2019, Pattern Recognit. Lett..

[32]  Yan Li,et al.  Segmentation of White Blood Cell from Acute Lymphoblastic Leukemia Images Using Dual-Threshold Method , 2016, Comput. Math. Methods Medicine.

[33]  Enas M. F. El Houby,et al.  Framework of Computer Aided Diagnosis Systems for Cancer Classification Based on Medical Images , 2018, J. Medical Syst..

[34]  Jan Zidek,et al.  Automated Detection of Acute Lymphoblastic Leukemia From Microscopic Images Based on Human Visual Perception , 2020, Frontiers in Bioengineering and Biotechnology.

[35]  Sos S. Agaian,et al.  Automated Screening System for Acute Myelogenous Leukemia Detection in Blood Microscopic Images , 2014, IEEE Systems Journal.

[36]  Erik Valdemar Cuevas Jiménez,et al.  An Improved Computer Vision Method for White Blood Cells Detection , 2013, Comput. Math. Methods Medicine.

[37]  F. Asadi,et al.  Deep Convolutional Neural Network–Based Computer-Aided Detection System for COVID-19 Using Multiple Lung Scans: Design and Implementation Study , 2021, Journal of Medical Internet Research.

[38]  H. S. Bhadauria,et al.  An approach for leukocytes nuclei segmentation based on image fusion , 2014, 2014 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

[39]  Farkhondeh Asadi,et al.  Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Systematic Review , 2020, Journal of healthcare engineering.

[40]  Muhammad Iqbal Hossain,et al.  Application of Homomorphic Encryption on Neural Network in Prediction of Acute Lymphoid Leukemia , 2020 .

[41]  Dipti Patra,et al.  An ensemble classifier system for early diagnosis of acute lymphoblastic leukemia in blood microscopic images , 2013, Neural Computing and Applications.

[42]  R. Flin,et al.  Developing a team performance framework for the intensive care unit* , 2009, Critical care medicine.

[43]  D. Goutam,et al.  Classification of acute myelogenous leukemia in blood microscopic images using supervised classifier , 2015, 2015 IEEE International Conference on Engineering and Technology (ICETECH).

[44]  Saeed Kermani,et al.  Recognition of Acute Lymphoblastic Leukemia Cells in Microscopic Images Using K-Means Clustering and Support Vector Machine Classifier , 2015, Journal of medical signals and sensors.

[45]  Lin Yang,et al.  Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review , 2016, IEEE Reviews in Biomedical Engineering.

[46]  F. Scotti,et al.  Robust Segmentation and Measurements Techniques of White Cells in Blood Microscope Images , 2006, 2006 IEEE Instrumentation and Measurement Technology Conference Proceedings.

[47]  Gloria Díaz.  Automatic Analysis of Microscopic Images in Hematological Cytology Applications , 2016 .

[48]  Cecilia Di Ruberto,et al.  Leucocyte classification for leukaemia detection using image processing techniques , 2014, Artif. Intell. Medicine.

[49]  N Chaitra,et al.  Automatic detection of acute lymphoblasitc leukemia using image processing , 2016, 2016 IEEE International Conference on Advances in Computer Applications (ICACA).

[50]  Hamid Soltanian-Zadeh,et al.  Automatic Recognition of Five Types of White Blood Cells in Peripheral Blood , 2010, ICIAR.

[51]  Ahmed H. Kandil,et al.  A decision support system for Acute Leukaemia classification based on digital microscopic images , 2017, Alexandria Engineering Journal.

[52]  Samabia Tehsin,et al.  Acute Lymphoblastic Leukemia Detection and Classification of Its Subtypes Using Pretrained Deep Convolutional Neural Networks , 2018, Technology in cancer research & treatment.

[53]  N. Poornima,et al.  Microscopic Image Segmentation Using Fuzzy C Means For Leukemia Diagnosis , 2017 .

[54]  Shibiao Wan,et al.  Machine Learning for Protein Subcellular Localization Prediction , 2015 .

[55]  Carlos Eduardo Pedreira,et al.  New Decision Support Tool for Treatment Intensity Choice in Childhood Acute Lymphoblastic Leukemia , 2009, IEEE Transactions on Information Technology in Biomedicine.

[56]  Himadri Sekhar Dutta,et al.  Nucleus and cytoplasm–based segmentation and actor-critic neural network for acute lymphocytic leukaemia detection in single cell blood smear images , 2019, Medical & Biological Engineering & Computing.

[57]  Shubhangi Khobragade,et al.  Detection of leukemia in microscopic white blood cell images , 2015, 2015 International Conference on Information Processing (ICIP).

[58]  José Rodellar,et al.  Automatic recognition of different types of acute leukaemia in peripheral blood by image analysis , 2019, Journal of Clinical Pathology.

[59]  S. Praveena,et al.  Sparse-FCM and Deep Convolutional Neural Network for the segmentation and classification of acute lymphoblastic leukaemia , 2020, Biomedizinische Technik. Biomedical engineering.

[60]  Nauman Aslam,et al.  An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images , 2015, Scientific Reports.

[61]  P. Landrigan.  Childhood leukemias. , 1995, The New England journal of medicine.

[62]  Emad A. Mohammed,et al.  Chronic lymphocytic leukemia cell segmentation from microscopic blood images using watershed algorithm and optimal thresholding , 2013, 2013 26th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE).

[63]  Romuere Rôdrigues Veloso e Silva,et al.  Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification , 2018, Eng. Appl. Artif. Intell..

[64]  Zixing Ye,et al.  Mixed phenotype acute leukemia. , 2014, Chinese medical journal.

[65]  Zuoyong Li,et al.  LeukocyteMask: An automated localization and segmentation method for leukocyte in blood smear images using deep neural networks , 2019, Journal of biophotonics.

[66]  P. Viswanathan,et al.  Fuzzy C Means Detection of Leukemia Based on Morphological Contour Segmentation , 2015 .

[67]  S. McCalla,et al.  Detection and Classification of Immature Leukocytes for Diagnosis of Acute Myeloid Leukemia Using Random Forest Algorithm , 2020, Bioengineering.

[68]  Yogesh Ambadas Gajul,et al.  Computerized Detection System for Acute Myelogenous Leukemia in Blood Microscopic Images , 2016 .

[69]  Vanika Singhal,et al.  Texture Features for the Detection of Acute Lymphoblastic Leukemia , 2016 .

[70]  L. Pedersen,et al.  Clinical epidemiology in the era of big data: new opportunities, familiar challenges , 2017, Clinical epidemiology.

[71]  Ahmad Taher Azar,et al.  Rough set theory with Jaya optimization for acute lymphoblastic leukemia classification , 2019, Neural Computing and Applications.

[72]  Kamal A. ElDahshan,et al.  An Efficient Implementation of Acute Lymphoblastic Leukemia Images segmentation on the FPGA , 2015 .

[73]  Shuai Liu,et al.  A segmentation method based on HMRF for the aided diagnosis of acute myeloid leukemia , 2017, Comput. Methods Programs Biomed..

[74]  S. A. Kareem,et al.  A Robust Feature Extraction and Selection Method for the Recognition of Lymphocytes versus Acute Lymphoblastic Leukemia , 2012, 2012 International Conference on Advanced Computer Science Applications and Technologies (ACSAT).

[75]  A. Talebi,et al.  Enhanced Recognition of Acute Lymphoblastic Leukemia Cells in Microscopic Images based on Feature Reduction using Principle Component Analysis , 2015 .

[76]  Zuoyong Li,et al.  Inter-class sparsity based discriminative least square regression , 2018, Neural Networks.