A Novel Automated Chromosome Analyzer Software Bundle for Karyotyping and Birth Defect Analysis
Karyotyping is a procedure to diagnose birth defects using chromosome pair. During the Karyotyping chromosomes arranged based on the length and each chromosome will be paired based on various parameters such as, chromosome length, banding pattern and Centromere position. Many methods are proposed to...
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| Fformat: | Erthygl |
| Iaith: | English |
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IEEE
2023-01-01
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| Cyfres: | IEEE Access |
| Pynciau: | |
| Mynediad Ar-lein: | https://ieeexplore.ieee.org/document/10364946/ |
| _version_ | 1827396100846256128 |
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| author | Devaraj Somasundaram Aditya Chapparadahallimath Muralitharan Krishanan Nirmala Madian |
| author_facet | Devaraj Somasundaram Aditya Chapparadahallimath Muralitharan Krishanan Nirmala Madian |
| author_sort | Devaraj Somasundaram |
| collection | DOAJ |
| description | Karyotyping is a procedure to diagnose birth defects using chromosome pair. During the Karyotyping chromosomes arranged based on the length and each chromosome will be paired based on various parameters such as, chromosome length, banding pattern and Centromere position. Many methods are proposed to identify the above parameters to improve the Karyotyping accuracy. Since, it’s a challenging task for researchers to improve the accuracy of Karyotyping compared with clinical assays. In this paper, a novel computer geometry method is proposed for chromosome Karyotyping using inbuilt deep learning models with algorithms for chromosome segmentation, overlapped separation, banding pattern analysis and classification of chromosomes. Chromosome classification is carried out using various deep learning models and automated Karyotyping is carried out without manual intervention and model improved the Karyotyping accuracy over the existing methods. In this paper novel computer geometry method is proposed to automate the chromosome analysis in Matlab environment. The developed software provides the accuracy of 99.68% in classification and karyotyping. |
| first_indexed | 2024-03-08T18:45:52Z |
| format | Article |
| id | doaj.art-3bb29435d9dc4f18acac3c1d9f878bcd |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T18:45:52Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3bb29435d9dc4f18acac3c1d9f878bcd2023-12-29T00:03:41ZengIEEEIEEE Access2169-35362023-01-011114551614552610.1109/ACCESS.2023.334466410364946A Novel Automated Chromosome Analyzer Software Bundle for Karyotyping and Birth Defect AnalysisDevaraj Somasundaram0https://orcid.org/0000-0002-7496-940XAditya Chapparadahallimath1Muralitharan Krishanan2Nirmala Madian3Department of Micro and Nano Electronics, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, IndiaDepartment of Micro and Nano Electronics, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, IndiaDepartment of Computer Science, Institute of Mathematical Sciences, Sungkyunkwan University, Suwon, South KoreaDepartment of Biomedical Engineering, Dr. N. G. P. Institute of Technology, Coimbatore, Tamilnadu, IndiaKaryotyping is a procedure to diagnose birth defects using chromosome pair. During the Karyotyping chromosomes arranged based on the length and each chromosome will be paired based on various parameters such as, chromosome length, banding pattern and Centromere position. Many methods are proposed to identify the above parameters to improve the Karyotyping accuracy. Since, it’s a challenging task for researchers to improve the accuracy of Karyotyping compared with clinical assays. In this paper, a novel computer geometry method is proposed for chromosome Karyotyping using inbuilt deep learning models with algorithms for chromosome segmentation, overlapped separation, banding pattern analysis and classification of chromosomes. Chromosome classification is carried out using various deep learning models and automated Karyotyping is carried out without manual intervention and model improved the Karyotyping accuracy over the existing methods. In this paper novel computer geometry method is proposed to automate the chromosome analysis in Matlab environment. The developed software provides the accuracy of 99.68% in classification and karyotyping.https://ieeexplore.ieee.org/document/10364946/Deep learningchromosomekaryotypingbanding pattern |
| spellingShingle | Devaraj Somasundaram Aditya Chapparadahallimath Muralitharan Krishanan Nirmala Madian A Novel Automated Chromosome Analyzer Software Bundle for Karyotyping and Birth Defect Analysis IEEE Access Deep learning chromosome karyotyping banding pattern |
| title | A Novel Automated Chromosome Analyzer Software Bundle for Karyotyping and Birth Defect Analysis |
| title_full | A Novel Automated Chromosome Analyzer Software Bundle for Karyotyping and Birth Defect Analysis |
| title_fullStr | A Novel Automated Chromosome Analyzer Software Bundle for Karyotyping and Birth Defect Analysis |
| title_full_unstemmed | A Novel Automated Chromosome Analyzer Software Bundle for Karyotyping and Birth Defect Analysis |
| title_short | A Novel Automated Chromosome Analyzer Software Bundle for Karyotyping and Birth Defect Analysis |
| title_sort | novel automated chromosome analyzer software bundle for karyotyping and birth defect analysis |
| topic | Deep learning chromosome karyotyping banding pattern |
| url | https://ieeexplore.ieee.org/document/10364946/ |
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