SRAS‐net: Low‐resolution chromosome image classification based on deep learning

Liu, Xiangbin; Fu, Lei; Lin, Jerry Chun‐Wei; Liu, Shuai

doi:10.1049/syb2.12042

Cited by 20 publications

(9 citation statements)

References 38 publications

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“…Xiao and Luo 18 proposed DeepACC system to classify chromosomes using a prior knowledge of Denver groups. Liu et al 19 developed hybrid SRAS‐net by combining super‐resolution network and self‐attention negative feedback network to classify chromosomes from low‐resolution metaphase images with 97.55% accuracy. Zhang et al 20 developed interleaved network for chromosome classification and CNN for chromosome joint detection, and multitask network is used from straightening of bent chromosome.…”

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%

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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“…Table 3 shows the total cases of normal, abnormal number, and structural abnormality in our data set. 7,10,21,29,30 . The total number of chromosomes and images in our data set is much higher than in the other data sets.…”

Section: Data Recordsmentioning

confidence: 99%

“…The total number of chromosomes and images in our data set is much higher than in the other data sets. Except for our data set and the SRAS-net data set 29 , the data sets used in the other studies are not publicly available. Although chromosome painting (e.g.…”

Section: Data Recordsmentioning

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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“…Image denoising can reduce the noise of the EM image while preserving the details of the image (Rahaman et al, 2020b ). Besides, in the process of deep learning based EMs image analyzation, we can extract the features of EM images, then send them to the deep learning network model for training, and match them with known data to classify, retrieve and detect EMs (Liu et al, 2022c ). In addition, EM images can also be applied in the field of image segmentation to separate microorganisms from the complex background of the image (Pal and Pal, 1993 ).…”

Section: Introductionmentioning

confidence: 99%

EMDS-7: Environmental microorganism image dataset seventh version for multiple object detection evaluation

Yang

Zhao

et al. 2023

Front. Microbiol.

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Nowadays, the detection of environmental microorganism indicators is essential for us to assess the degree of pollution, but the traditional detection methods consume a lot of manpower and material resources. Therefore, it is necessary for us to make microbial data sets to be used in artificial intelligence. The Environmental Microorganism Image Dataset Seventh Version (EMDS-7) is a microscopic image data set that is applied in the field of multi-object detection of artificial intelligence. This method reduces the chemicals, manpower and equipment used in the process of detecting microorganisms. EMDS-7 including the original Environmental Microorganism (EM) images and the corresponding object labeling files in “.XML” format file. The EMDS-7 data set consists of 41 types of EMs, which has a total of 2,65 images and 13,216 labeled objects. The EMDS-7 database mainly focuses on the object detection. In order to prove the effectiveness of EMDS-7, we select the most commonly used deep learning methods (Faster-Region Convolutional Neural Network (Faster-RCNN), YOLOv3, YOLOv4, SSD, and RetinaNet) and evaluation indices for testing and evaluation. EMDS-7 is freely published for non-commercial purpose at: https://figshare.com/articles/dataset/EMDS-7_DataSet/16869571.

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SRAS‐net: Low‐resolution chromosome image classification based on deep learning

Cited by 20 publications

References 38 publications

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

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

An Open Dataset of Annotated Metaphase Cell Images for Chromosome Identification

EMDS-7: Environmental microorganism image dataset seventh version for multiple object detection evaluation

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