Chromosomes are thread-like structures located in the cell nucleus that contains the human body blueprint. Chromosome analysis is also known as karyotyping is the test taken to detect the abnormalities identified in the human chromosome. The two types of widely known chromosome abnormalities are structural and numerical abnormalities. Manual karyotyping is complex, time-consuming, and error-prone. To overcome these complexities, automated chromosome karyotype architecture is proposed using the deep convolutional neural network (DCNN) architecture. Training the DCNN architecture from scratch needs a huge dataset and to overcome this problem a generative adversarial networks is used to create adversarial samples that resemble the images in the actual dataset. The time-consuming hyperparameter tuning in the DCNN architecture is overcome using the hybrid moth-flame optimization integrated with the hill-climbing strategy (HMFOHC). The HMFOHC algorithm is mainly utilized in this article to minimize the huge number of parameters associated with the DCNN architecture. The efficiency of the proposed methodology is evaluated using two datasets namely the BioImLab chromosome dataset and hospital dataset. The proposed HMFOHC optimized DCNN architecture is mainly utilized for multiclass classification where it differentiates five numerical chromosome abnormalities, namely Trisomy 13, Trisomy 18, Trisomy 21, Trisomy XXY syndrome, and Monosomy X. The proposed model offers an accuracy, F1-score, and kappa coefficient value of 98.65%, 98.86%, and 0.9894, respectively. The results obtained show that the proposed model achieves higher classification accuracy when compared with the different state-of-art techniques such as deep learning, random forest, and CNN. The inference time of the proposed methodology is 12.5 s which is relatively lower than the state-of-art techniques. The proposed approach can help cytogenetics forensic experts make better decisions and save time by automating manual karyotyping.

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基于混合优化模型的染色体异常检测的改进深度卷积神经网络架构

染色体是位于包含人体蓝图的细胞核中的线状结构。染色体分析也称为核型分析，是用于检测人类染色体中发现的异常的测试。两种广为人知的染色体异常是结构异常和数量异常。手动核型分析复杂、耗时且容易出错。为了克服这些复杂性，提出了使用深度卷积神经网络 (DCNN) 架构的自动染色体核型架构。从头开始训练 DCNN 架构需要一个庞大的数据集，为了克服这个问题，使用生成对抗网络来创建与实际数据集中图像相似的对抗样本。使用结合爬山策略 (HMFOHC) 的混合蛾火焰优化来克服 DCNN 架构中耗时的超参数调整。本文主要使用 HMFOHC 算法来最小化与 DCNN 架构相关的大量参数。使用两个数据集评估所提出方法的效率，即 BioImLab 染色体数据集和医院数据集。所提出的 HMFOHC 优化的 DCNN 架构主要用于多类分类，其中它区分了五种数字染色体异常，即 13 三体、18 三体、21 三体、XXY 三体综合征和 X 单体。所提出的模型提供了准确度，本文主要使用 HMFOHC 算法来最小化与 DCNN 架构相关的大量参数。使用两个数据集评估所提出方法的效率，即 BioImLab 染色体数据集和医院数据集。所提出的 HMFOHC 优化的 DCNN 架构主要用于多类分类，其中它区分了五种数字染色体异常，即 13 三体、18 三体、21 三体、XXY 三体综合征和 X 单体。所提出的模型提供了准确度，本文主要使用 HMFOHC 算法来最小化与 DCNN 架构相关的大量参数。使用两个数据集评估所提出方法的效率，即 BioImLab 染色体数据集和医院数据集。所提出的 HMFOHC 优化的 DCNN 架构主要用于多类分类，其中它区分了五种数字染色体异常，即 13 三体、18 三体、21 三体、XXY 三体综合征和 X 单体。所提出的模型提供了准确度，F 1 分，kappa 系数值分别为 98.65%、98.86% 和 0.9894。获得的结果表明，与深度学习、随机森林和 CNN 等不同的最新技术相比，所提出的模型实现了更高的分类精度。所提出方法的推理时间为 12.5 秒，相对低于最先进的技术。所提出的方法可以帮助细胞遗传学法医专家通过自动化手动核型分析做出更好的决策并节省时间。