Designing A Two-Stage Classification Network Algorithm for Acute Lymphocytic Leukemia Diagnosis in Blood Lamella Images.
Background and Aim: Diagnosis of leukemia is very difficult, therefore, it is necessary to use image processing techniques. The main objective of this study was to provide a system based on intelligent models that could improve the accuracy of the diagnostic system for acute leukemia. Materials and...
| Published in: | Arak Medical University Journal Vol. 22; no. 1; pp. 108 - 115 |
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| Main Authors: | , , |
| Format: | diagnostic images research tables/charts Journal Article |
| Published: |
Arak University of Medical Sciences
2019
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=136500148&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136500148 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17355338 B2BM jtl: Arak Medical University Journal issn: 17355338 maglogo: N pubinfo: dt: 2019 vid: 22 iid: 1 pid: 59841 pub: Arak University of Medical Sciences artinfo: ui: 136500148 136500148 136500148 136500148 ppf: 108 ppct: 7 formats: tig: atl: Designing A Two-Stage Classification Network Algorithm for Acute Lymphocytic Leukemia Diagnosis in Blood Lamella Images. aug: au: Zamani, Arman Babaei, Abolghasem Sadat Mostafavi, Nayyer affil: Department of Medical Equipment, Arak Ayatollah Khansari Hospital, Arak, Iran sug: subj: Algorithms Neural Networks (Computer) Leukemia, Lymphocytic, Acute Diagnosis Diagnostic Imaging Standards Quality Improvement Human Descriptive Statistics Software Image Processing, Computer Assisted ab: Background and Aim: Diagnosis of leukemia is very difficult, therefore, it is necessary to use image processing techniques. The main objective of this study was to provide a system based on intelligent models that could improve the accuracy of the diagnostic system for acute leukemia. Materials and Methods: The images produced in this study were extracted from the University Degli Studi Dimilan database and processed in the MATlab 2014a software. In this research, Fuzzy-Cmeans method was used in fragmentation and neural network and support vector machine in classification networks. Ethical Considerations: In this study, all principles of research ethics were considered. Findings: Feature data were extracted using the original image transfer to RGB, HSV, Lab and Enhanced RGB spaces. The data obtained from the previous step were entered into the SVM network, then the network separated normal data from abnormal data. The results of comparing the output of the proposed method with different educational methods showed the highest mean of accuracy equal to 95.7%. Conclusion: The application of the proposed network in this study was that eliminate the weak points of all the networks in addition to presenting the advantages of these network. Combining the networks improved the accuracy of output up to 98% and considerably reduced the time required for calculations pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: Persian refInfo: holdings: @attributes: islocal: N |
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