Improving Classification of Curved Chromosomes in Karyotyping using CNN-based Deformation

Nguyen, Quan A.; Nguyen, Nhung T. C.; Nguyen, Son H.H.; Doan, Phuong T. K.; Thinh, Nguyen H.; Tran, Tung H.; Luong, Anh T. L.; Le, Ha V.; Luu, Ha M.

doi:10.1109/ssp53291.2023.10208061

Cited by 1 publication

(1 citation statement)

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“…EpilepsyNet is simple, computationally inexpensive deep learning‐based transformer framework is proposed for classification of EEG signals 34,35 . Nguyen et al 36 proposed spatial transformer network (SPN) for chromosome straightening and analyzed its effect on CNN‐based chromosome classification. Joshi et al 37 developed chromosome cluster transformer (CCT) for automatic analysis of karyotype for identification of genetic disorders.…”

Section: Literature Reviewmentioning

confidence: 99%

Karyotyping of human chromosomes in metaphase images using faster R‐CNN and inception models

Chavan,

Nair,

Nimbalkar

et al. 2024

Int J Imaging Syst Tech

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Karyotyping is the process of pairing and ordering human chromosomes from metaphase chromosomal images depending on their size, centromere position, and banding patterns. It is used to analyze human chromosomes for various genetic disorders especially during prenatal screenings. Since manual karyotyping is a labor‐intensive and a time‐consuming task, developing an automatic or semi‐automatic computer‐assisted karyotyping system is the need of the hour. The proposed karyotyping system aims to detect human chromosomes (22 pairs of autosomes and one pair of sex chromosome) from the microscopic metaphase images which is followed by separation of overlapped chromosomes. Extracted chromosomes are first classified into seven Denver groups (A to G) followed by classifying individual chromosomes within their Denver group. Then the classified chromosome pairs are used to create a karyotype with the assistance of cytologists or by using estimated chromosome length as cytogenetic parameter. A total of 234 chromosomal images from two different public datasets are used for experimentation. Human chromosomes in the microscopic metaphase images are detected using faster regions with convolutional neural networks (Faster R‐CNN) combined with Inception v2. Then convexity defect algorithm is preferred for separation of overlapped chromosomes. The detected chromosomes are classified using the proposed two‐step approach in which Inception v3 model is used. Then the classified chromosome pairs are used to create a karyotype with the assistance of cytologists or by using estimated chromosome length as cytogenetic parameter. Faster R‐CNN model gives a detection accuracy of 98.53%. Denver group classification with the two‐step approach provides a better accuracy of 84.59% when network is trained for 64 902 epochs. Faster R‐CNN outperforms in detection and works faster as it searches chromosomes within the regional proposals. The two‐step classification approach gives better classification accuracy. The proposed approach also works very well for overlapping chromosomes due to use of convexity defects algorithm. The classified chromosome pairs are useful tools for the cytologists to create a karyotype with minimal efforts.

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Section: Literature Reviewmentioning

confidence: 99%