Automatic Acute Lymphoblastic Leukemia Detection and Comparative Analysis from Images

In this era, surrounded by numerous technologies, medical sector has seen a lot of advancement through implementing various autonomous systems to identify different types of diseases. In this paper, a framework for identification of Acute Lymphoblastic Leukemia from the microscopic image of white blood cell is proposed. Microscopic images are at first carefully preprocessed to prepare them for classification. In addition, four different machine learning algorithms, namely, Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), and Decision Tree (DT) are applied and respective results are analyzed to provide a comparison between these algorithms in terms of different performance metrics. After a thorough comparison, it is observed that the SVM works well to classify and identify the Acute Lymphoblastic cell which is responsible for Leukemia cancer.

[1]  Vassili A. Kovalev,et al.  Robust recognition of white blood cell images , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[2]  Osman Selcuk,et al.  Acute lymphoblastic leukemia diagnosis using image processing techniques , 2015, 2015 23nd Signal Processing and Communications Applications Conference (SIU).

[3]  Agus Harjoko,et al.  Automated detection and classification techniques of Acute leukemia using image processing: A review , 2016, 2016 2nd International Conference on Science and Technology-Computer (ICST).

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

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

[6]  Dorin Comaniciu,et al.  Computer-assisted discrimination among malignant lymphomas and leukemia using immunophenotyping, intelligent image repositories, and telemicroscopy , 2000, IEEE Transactions on Information Technology in Biomedicine.

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

[8]  Wael M. Badawy,et al.  A High Throughput Screening Algorithm for Leukemia Cells , 2006, 2006 Canadian Conference on Electrical and Computer Engineering.

[9]  H. S. Bhadauria,et al.  Review of leukocyte classification techniques for microscopic blood images , 2015, 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom).

[10]  V. Piuri,et al.  Morphological classification of blood leucocytes by microscope images , 2004, 2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA..

[11]  Madhumita Das,et al.  An analytical approach for leukemia diagnosis from light microscopic images of Rbcs (Computational approach for leukemia diagnosis) , 2016, 2016 2nd International Conference on Next Generation Computing Technologies (NGCT).

[12]  Aladin Zayegh,et al.  K-Means Clustering on 3rd order polynomial based normalization of Acute Myeloid Leukemia (AML) and Acute Lymphocyte Leukemia (ALL) , 2009, 2009 Third International Conference on Electrical Engineering.

[13]  D. Corda,et al.  Identification and classification of acute lymphoblastic leukemia cells from peripheral blood by using Raman spectroscopy , 2016 .

[14]  K. S. Kim,et al.  Analyzing blood cell image to distinguish its abnormalities (poster session) , 2000, ACM Multimedia.

[15]  Sheau-Ling Hsieh,et al.  Leukemia cancer classification based on Support Vector Machine , 2010, 2010 8th IEEE International Conference on Industrial Informatics.

[16]  Dipti Patra,et al.  Automated leukemia detection in blood microscopic images using statistical texture analysis , 2011, ICCCS '11.

[17]  Preeti Jagadev,et al.  Detection of leukemia and its types using image processing and machine learning , 2017, 2017 International Conference on Trends in Electronics and Informatics (ICEI).

[18]  B Ananya,et al.  Novel Approach to Find the Various Stages of Chronic Myeloid Leukemia Using Dynamic Short Distance Pattern Matching Algorithm , 2018, 2018 3rd International Conference for Convergence in Technology (I2CT).

[19]  Kaoru Hirota,et al.  Interest-Based Ordering for Fuzzy Morphology on White Blood Cell Image Segmentation , 2012, J. Adv. Comput. Intell. Intell. Informatics.

[20]  Sini Shibu,et al.  Analysis of blood samples for counting leukemia cells using Support vector machine and nearest neighbour , 2014 .

[21]  Ulrich Brunsmann,et al.  FPGA-Based Real-Time Pedestrian Detection on High-Resolution Images , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[22]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Vincenzo Piuri,et al.  All-IDB: The acute lymphoblastic leukemia image database for image processing , 2011, 2011 18th IEEE International Conference on Image Processing.