Human Chromosome Classification Research Articles

Deep-Learning-Based Human Chromosome Classification: Data Augmentation and Ensemble

Object classification is a crucial task in deep learning, which involves the identification and categorization of objects in images or videos. Although humans can easily recognize common objects, such as cars, animals, or plants, performing this task on a large scale can be time-consuming and error-prone. Therefore, automating this process using neural networks can save time and effort while achieving higher accuracy. Our study focuses on the classification step of human chromosome karyotyping, an important medical procedure that helps diagnose genetic disorders. Traditionally, this task is performed manually by expert cytologists, which is a time-consuming process that requires specialized medical skills. Therefore, automating it through deep learning can be immensely useful. To accomplish this, we implemented and adapted existing preprocessing and data augmentation techniques to prepare the chromosome images for classification. We used ResNet-50 convolutional neural network and Swin Transformer, coupled with an ensemble approach to classify the chromosomes, obtaining state-of-the-art performance in the tested dataset.

Toward Human Chromosome Knowledge Engine

Human chromosomes carry genetic information about our life. Chromosome classification is crucial for karyotype analysis. Existing chromosome classification methods do not take into account reasoning, such as: analyzing the relationship between variables, modeling uncertainty, and performing causal reasoning. In this paper, we introduce a knowledge engine for reasoning-based human chromosome classification that stores knowledge of chromosomes via a novel representation structure, the Chromosome Part Description (CPD), and reasons over CPDs by utilizing the probability tree model (PTM) for classification. Each CPD keeps information on a particular feature of chromosomes, while the PTM provides causal reasoning capability taking CPDs as nodes and dependencies between CPDs and types as edges. Experimental results show that the proposed knowledge engine’s performance increases when providing more CPDs and achieves 100% classification accuracy with more than three CPDs.

Extended ResNet and Label Feature Vector Based Chromosome Classification

Human chromosome classification is essential to the clinical diagnosis of cytogenetical diseases such as genetic disorders and cancer. This process, however, is time-consuming and requires specialist knowledge. Considerable efforts have been made to automat the process. Recently, methods based on Convolutional Neural Networks achieved state-of-the-art results on the chromosome classification task. Many studies used karyotype images in performance evaluation, few studies have reported the results of human chromosome classification on microscopical images. This paper proposes a novel method to classify single chromosome images into one of 24 types. In the proposed method an extended ResNet was first devised to extract features of single chromosome images. A label feature vector was then extracted for each of 24 chromosome types based on a validation dataset. Hausdorff distance between feature vector of an input image and each of 24 label feature vectors were calculated, and the label feature vector that has minimum hausdorff distance to the feature vector of the input image was selected as the potential label of the input image. To finally allocate the single chromosomes from a same microscopical image into one of 24 types, a Label Redistribution strategy was used to shrink the label space and to increase the efficiency of chromosome classification. Experiments were implemented with 90,624 single chromosome images, 644 of which were randomly picked to form a testing set in advance. The classification accuracy on microscopical images using our proposed method achieved an accuracy of 94.72%.

A system on chip for automatic karyotyping system

The Automatic karyotyping System is a computer-aided tool that automates the chromosome analysis and karyotyping processes, manually performed in most cytogenetic laboratories. Artificial neural networks (ANNs) have been widely used in chromosome classification due to their parallelism that reduces the computational complexity and time. However, existing classifiers are software-based, running on a computer that transforms the parallelism features of the ANNs into serial operations, thus significantly reducing their computing power. To efficiently address the above issue due to software implementation, we propose a Field-Programmable Gate Array-based System on Chip (SoC) architecture for human chromosome classification. The hardware implementation of such system can achieve the parallelism inherent to ANNs while reducing the power consumption and circuit size, thus the cost of designing such a system is reduced. The achieved part concerns the classification subsystem based on Kohonen neural network, which has been successfully tested on FPGA platform.

Towards an autonomous human chromosome classification system using Competitive Support Vector Machines Teams (CSVMT)

Abstract In broad terms, karyotyping is the process of examination and classification of human chromosome images to diagnose genetic diseases and disorders. It requires time consuming manual examination of cell images by a cytogeneticist to distinguish chromosome classes from each other. Thus, a reliable autonomous human chromosome classification system not only saves time and money but also reduces errors due to the inadequate knowledge level of the expert. Human cell contains 23 pairs of chromosome, 22 autosomes and a pair of sex chromosomes. Hence, we face a multi-class classification task which represents a challenging case for any sort of classifier. In this work, to solve this classification problem, we propose a novel methodology consisting two stages: (i) data preparation and training, and (ii) testing. To determine the most informative content of the dataset several preliminary experiments are conducted and a Principal Component Analysis is done. Then, a single Support Vector Machine (SVM ij ) is trained to separate a pair of classes, (i,j) where a numerical optimization method Pattern Search (PS), is employed to find the optimal parameters for the SVM ij . Considering 22 pairs of autosomes, 22 × 22 experts are trained and optimized. The cluster of experts, we obtain is named as Competitive SVM Teams (CSVMTs) where each SVM ij competes with the others to label a new classification instance. The final output of the classifier is determined by majority voteing. The results obtained on Copenhagen dataset proves the merit of the algorithm as correct classification rates (CRR) on train and test samples are 99.55% and 97.84% respectively, which are higher than any accuracy rate achieved so far in the related literature.

A survey of neural network based automated systems for human chromosome classification

Chromosome classification and karyotype establishment are important procedures for genetic diseases diagnosis. Various computer-aided systems have been developed to automate this tedious and time consuming task, which is performed manually in most cytogenetic laboratories. This paper provides a comprehensive review of past and recent research in the area of automatic chromosome classification systems. We start by reviewing methods for feature extraction, followed by a neural network based chromosome classifiers survey. We sum-up various techniques and methods in this area of research and discuss important issues and outcomes within each study for both chromosome feature extraction and classification. Although the ANN based chromosome classifiers are the main topic of this survey, a number of classifiers based on other algorithms are exposed to give an overall idea about additional techniques employed in chromosome classification.

Human chromosome classification using competitive support vector machine teams

Classification of chromosome is a challenging task and requires very precise autonomous classifier. This paper proposes to employ competing support vector machines (SVMs) placed in a grid. Each agent in cells of the grid is responsible to distinguish two classes. Overall output is determined by simple majority voting of SVMs. Relying same principle as the work by Palalic and Can [17], we compared the results obtained where the algorithms delivers better accuracy.

Medial axis transform-based features and a neural network for human chromosome classification

Medial axis transform (MAT) based features and a multilayer perceptron (MLP) neural network (NN) were used for human chromosome classification. Two approaches to the MAT, one based on skeletonization and the other based on a piecewise linear (PWL) approximation, were examined. The former yielded a finer medial axis, as well as better chromosome classification performances. Geometrical along with intensity-based features were extracted and tested. The probability of correct training set classification of five chromosome types was 99.3–99.6%. The probability of correct test set classification was greater than 98% and greater than 97% using features extracted by the first and second approaches, respectively. It was found that only 5–10, out of all the considered features, were required to correctly classify the chromosomes with almost no performance degradation.

HUMAN CHROMOSOME CLASSIFICATION USING MULTILAYER PERCEPTRON NEURAL NETWORK

A multilayer perceptron (MLP) neural network (NN) has been studied for human chromosome classification. Only 10-20 examples were required for the MLP NN to reach its ultimate performance classifying chromosomes of 5 types. The empirical dependence of the entropic error on the number of examples was found to be highly comparable to the 1/t function. The principal component analysis (PCA) was used, both for network initialization and for feature reduction purposes. The PCA demonstrated the importance of retaining most of the image information whenever small training sets are used. The MLP NN classifier outperformed the Bayes piecewise classifier for all the cases tested. The MLP classifier was found to be almost unsusceptible to the ratio of the number of training vectors to the number of features, whereas the piecewise classifier was highly dependent on this ratio.

Human chromosome classification based on local band descriptors

In this paper several new techniques for automated chromosome analysis are described: one for piecewise-linear chromosome stretching and projection, two for accurately localizing the centromere and one for two-dimensional local band pattern description. A classification procedure is described that is based upon local band descriptors. Classification results obtained with this method are compared with results obtained with the global band description method (WDD functions). Data sets from two different laboratories are used to investigate the influence of the preparation. Results show the suitability of the local description method in its ability to visualize the image processing technique at the level of the chromosome image.

Human chromosome classification using discriminant analysis and Bayesian probability

A computer-aided method of karyotyping human chromosomes was presented which utilized the probability of classification in a discriminant analysis as a measure of the confidence of correct identification. The program assigned each chromosome to one of two partial karyotypes based on a high or low probability of identification. A secondary discriminant analysis classification was then carried out on chromosomes identified with low confidence to determine whether additional chromosomes could then be reclassified with a higher level of confidence. The method was tested using 20 GTG-banded cells and 44 RFA-banded cells. The latter were divided into a 34-cell learning set and a 10-cell test set. Among the GTG-banded chromosomes used, 90.6% were classified with high confidence; of these, 93.5% were correct. A total of 88.6% were correct, however, if the confidence level was not considered. 77.3% of the RFA-banded learning set and 75.2% of the RFA-banded test set chromosomes used were classified with high confidence; 94.1% and 85.8%, respectively, were correct. If the confidence level was not considered, 86.1% of the learning set and 79.7% of the test set chromosomes were correctly identified.