An improved denoising of G-banding chromosome images using cascaded CNN and binary classification network

Chromosome analysis plays an important role in detecting genetic disorders. However, it is time-consuming when it is done manually. The first step for an automated solution is removing the background noise in the chromosome images. Denoising is studied by many researchers; however, it is still a challenging task due to contrast issues, blotches, and non-chromosome objects. In this paper, we proposed a cascaded neural network architecture for denoising G-banding chromosomes images. The proposed method consists of two steps. The first step is the initial segmentation network which combines the capabilities of U-net and residual units. The second step is the classification block, which is implemented in order to automate the denoising process and reduce the pixel losses on the chromosomes. The results showed that the proposed segmentation network achieves a higher dice score compared to state-of-the-art semantic segmentation neural networks, and the classification block greatly reduces the pixel losses on the chromosomes. We tested the proposed denoising method on 84 G-banding chromosome images and achieved a 98.74% dice score. Our automated denoising method outperformed the methods presented in previous studies and state-of-the-art methods. It can help cytogeneticists with repetitive work and provide them more accurate chromosomes for further evaluation.