A Comprehensive Study on Convolutional Neural Networks for Chromosome Classification

Abstract

Cytogenetics plays significant role in the diagnosis, prognosis and treatment evaluation of genetic disorders through chromosome image analysis technique called karyotyping. Karyotyping is the way by which chromosomes are classified into 24 classes. Digital image processing techniques and machine learning algorithms found its scope in automated karyotyping since they ease or eliminate manual efforts in chromosome classification and its analysis. Even though, researchers were putting great efforts in the design of Automated Karyotyping System (AKS), for the last three decades, a fully automated system is not yet routinely accepted in practice. These days, deepnets exhibit improved performance in computer vision tasks, they are progressively utilized for automating classification tasks as well. Here, two variants of deep Convolutional Neural Networks (CNNs) for chromosome classification are modelled. A preliminary study on the hyperparameters of these models has been conducted. Other state-of-the-art CNN models are experimented and analyzed for chromosome classification. Performance measures of all these CNN deep models are compared to formulate hypotheses on hyperparameters to classify chromosomes efficiently.