Literature survey of chromosomes classification and anomaly detection using machine learning algorithms

Nimitha, N; Arun, C.; Puvaneswari, AS; Paninila, B; Pavithra, V.; Pavithra, B.

doi:10.1088/1757-899x/402/1/012194

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…Most current systems are based on artificial intelligence (AI) approaches involving machine learning and deep learning [4][5][6][7] . Earlier studies on chromosome classification were based on segmenting overlaps and adherent chromosomes and employed conventional methods like border detection 8,9 , the watershed method 10 , and straightening of bent chromosomes 11,12 . These methods depended heavily on image preprocessing, resulting in distorted chromosome features that could result in misdiagnoses.…”

Section: Background and Summarymentioning

confidence: 99%

An Open Dataset of Annotated Metaphase Cell Images for Chromosome Identification

Tseng

et al. 2023

Sci Data

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Chromosomes are a principal target of clinical cytogenetic studies. While chromosomal analysis is an integral part of prenatal care, the conventional manual identification of chromosomes in images is time-consuming and costly. This study developed a chromosome detector that uses deep learning and that achieved an accuracy of 98.88% in chromosomal identification. Specifically, we compiled and made available a large and publicly accessible database containing chromosome images and annotations for training chromosome detectors. The database contains five thousand 24 chromosome class annotations and 2,000 single chromosome annotations. This database also contains examples of chromosome variations. Our database provides a reference for researchers in this field and may help expedite the development of clinical applications.

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