Automatic Classification of Low-Resolution Chromosomal Images

Abstract

Chromosome karyotyping is a two-staged process consisting of segmentation followed by pairing and ordering of 23 pairs of human chromosomes obtained from cell spread images during metaphase stage of cell division. It is carried out by cytogeneticists in clinical labs on the basis of length, centromere position, and banding pattern of chromosomes for the diagnosis of various health and genetic disorders. The entire process demands high domain expertise and considerable amount of manual effort. This motivates us to automate or partially automate karyotyping process which would benefit and aid doctors in the analysis of chromosome images. However, the non-availability of high resolution chromosome images required for classification purpose creates a hindrance in achieving high classification accuracy. To address this issue, we propose a Super-Xception network which takes the low-resolution chromosome images as input and classifies them to one of the 24 chromosome class labels after conversion into high resolution images. In this network, we integrate super-resolution deep models with standard classification networks e.g., Xception network in our case. The network is trained in an end-to-end manner in which the super-resolution layers help in conversion of low-resolution images to high-resolution images which are subsequently passed through deep classification layers for label assigning. We evaluate our proposed network’s efficacy on a publicly available online Bioimage chromosome classification dataset of healthy chromosomes and benchmark it against the baseline models created using traditional deep convolutional neural network, ResNet-50 and Xception network.