Human chromosome classification using Competitive Neural Network Teams (CNNT) and Nearest Neighbor

Gagula-Palalic, Sadina; Can, Mehmet

doi:10.1109/bhi.2014.6864442

Cited by 9 publications

(2 citation statements)

References 16 publications

(18 reference statements)

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“…The average accuracy of their proposed work was 94% and the average run time was 3s. Gagula-Palalic & Can [31] proposed a Competitive Neural Network Teams (CNNTs) that ensemble of ANN and nearest neighbor classifiers. This method consists of 462 simple perceptrons.…”

Section: Bchromosomes Classificationmentioning

confidence: 99%

Automated System for Chromosome Karyotyping to Recognize the Most Common Numerical Abnormalities Using Deep Learning

2020

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Chromosome analysis is an essential task in a cytogenetics lab, where cytogeneticists can diagnose whether there are abnormalities or not. Karyotyping is a standard technique in chromosome analysis that classifies metaphase image to 24 chromosome classes. The main two categories of chromosome abnormalities are structural abnormalities that are changing in the structure of chromosomes and numerical abnormalities which include either monosomy (missing one chromosome) or trisomy (extra copy of the chromosome). Manual karyotyping is complex and requires high domain expertise, as it takes an amount of time. With these motivations, in this research, we used deep learning to automate karyotyping to recognize the common numerical abnormalities on a dataset containing 147 non-overlapped metaphase images collected from the Center of Excellence in Genomic Medicine Research at King Abdulaziz University. The metaphase images went through three stages. The first one is individual chromosomes detection using YOLOv2 Convolutional Neural Network followed by some chromosome post-processing. This step achieved 0.84 mean IoU, 0.9923 AP, and 100% individual chromosomes detection accuracy. The second stage is feature extraction and classification where we fine-tune VGG19 network using two different approaches, one by adding extra fully connected layer(s) and another by replacing fully connected layers with the global average pooling layer. The best accuracy obtained is 95.04%. The final step is detecting abnormality and this step obtained 96.67% abnormality detection accuracy. To further validate the proposed classification method, we examined the Biomedical Imaging Laboratory dataset which is publicly available online and achieved 94.11% accuracy.

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Section: Bchromosomes Classificationmentioning

confidence: 99%

Automated System for Chromosome Karyotyping to Recognize the Most Common Numerical Abnormalities Using Deep Learning

2020

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“…Sharma et al 15 proposed a model with segmentation by crowd sourcing and classification using deep convolutional neural network. Gagula‐Palalic and Can 16 and Kusakci et al 17 proposed a competitive neural network teams (CNNT) and competitive support vector machines teams (CSVMT) for classification of 22 autosomes, respectively. Xiao and Luo 18 proposed DeepACC system to classify chromosomes using a prior knowledge of Denver groups.…”

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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Chromosome Medial Axis Extraction Method Based on Graphic Geometry and Competitive Extreme Learning Machines Teams (CELMT) Classifier for Chromosome Classification

Wang

Zhao

Liang

et al. 2020

Communications in Computer and Information Science

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Human chromosome classification using Competitive Neural Network Teams (CNNT) and Nearest Neighbor

Cited by 9 publications

References 16 publications

Automated System for Chromosome Karyotyping to Recognize the Most Common Numerical Abnormalities Using Deep Learning

Automated System for Chromosome Karyotyping to Recognize the Most Common Numerical Abnormalities Using Deep Learning

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

Chromosome Medial Axis Extraction Method Based on Graphic Geometry and Competitive Extreme Learning Machines Teams (CELMT) Classifier for Chromosome Classification

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