The classification of chromosome images holds immense significance in the fields of genetics, clinical diagnostics, and medical research. It plays a pivotal role in the precise identification of genetic abnormalities, allowing for early and accurate diagnosis of various genetic disorders and birth defects. The automation of this process offers significant advantages in terms of time and human resource savings. This study introduces the Model Architecture Search System (MASS), designed to adapt itself to classify chromosome images for karyotyping. The MASS framework aims to construct an optimal model architecture for the specific classification task by leveraging predefined CNN backbones, activation functions, and loss functions. There are 12 pre-trained networks, 5 activation functions, and 2 loss functions in the selection set of the MASS. The proposed framework utilizes the Tree-structured Parzen Estimator (TPE) algorithm based on Bayesian Optimization, eliminating the need for manual model searching processes and finding optimal model architecture. The suitable model structure for the relevant dataset is generated from these groups automatically by using TPE. Experiments conducted on two distinct datasets demonstrate the superior performance achieved by this proposed mechanism.

染色体图像的分类在遗传学、临床诊断和医学研究领域具有重要意义。它在精确识别遗传异常方面发挥着关键作用，可以对各种遗传性疾病和出生缺陷进行早期准确诊断。该过程的自动化在节省时间和人力资源方面提供了显着的优势。本研究介绍了模型架构搜索系统 (MASS)，该系统旨在适应对染色体图像进行核型分析的分类。MASS 框架旨在通过利用预定义的 CNN 主干、激活函数和损失函数，为特定分类任务构建最佳模型架构。MASS的选择集中有12个预训练网络、5个激活函数和2个损失函数。所提出的框架利用基于贝叶斯优化的树结构 Parzen 估计器 (TPE) 算法，消除了手动模型搜索过程和寻找最佳模型架构的需要。使用 TPE 从这些组中自动生成适合相关数据集的模型结构。在两个不同的数据集上进行的实验证明了该机制所实现的卓越性能。