MixNet: A Better Promising Approach for Chromosome Classification based on Aggregated Residual Architecture

Chromosome classification is an important way of clinical genetic diagnosis. However, it is usually done manually by skilled cytologists, which requires extensive experience, domain knowledge, and tedious manual work. In this paper, we tackle the task of chromosome classification utilizing deep learning techniques based on aggregated residual architecture. We proposed a novel chromosome classification method named MixNet. Firstly, we design a deep learning-based chromosome classification framework that consists of a chromosome encoding backbone and an adaptive header. Secondly, we design the chromosome backbone utilizing the aggregated residual architecture and propose the adaptive header by aggregating pooling layers to classify chromosome latent features. Lastly, we propose a training algorithm to fine-tune MixNet. The experimental results demonstrate that our proposed method yields a mean classification accuracy of 98.73% with 0.81 standard deviations on our G-band clinical dataset and a mean classification accuracy of 96.50% with 1.87 standard deviations on public Q-band dataset. To our best knowledge, it is the state-of-the-art result of the public Q-band dataset.