Banded chromosome images recognition based on dense convolutional network with segmental recalibration

Abstract

Human chromosomes karyotyping is an important means to diagnose genetic diseases. Chromosome image type recognition is a key step in the karyotyping process. Accurate and efficient identification is of great significance for automatic chromosome karyotyping. In this paper, we propose a model named segmentally recalibrated dense convolutional network (SR-DenseNet). In each stage of the model, the dense connected network layers is used to extract the features of different abstract levels of chromosomes automatically, and then the concatenation of all the layers which extract different local features is recalibrated with squeeze-and-excitation (SE) block. SE blocks explicitly construct learnable structures for importance of the features. Then a model fusion method is proposed and an expert group of chromosome recognition models is constructed. On the public available Copenhagen chromosome recognition dataset (G-bands) the proposed model achieves error rate of only 1.60%, and with model fusion the error further drops to 0.99%. On the Padova chromosome dataset (Q-bands) the model gets the corresponding error rate of 6.67%, and with model fusion the error further drops to 5.98%. The experimental results show that the method proposed in this paper is effective and has the potential to realize the automation of chromosome type recognition.