Automatic Enhancement of Two-Dimensional Gel electrophoresis images using Denoising Autoencoder

Image denoising is an important preprocessing step in two-dimensional gel electrophoresis (2-DGE) that strongly affect spot detection or pixel-based methods. Denoising autoen-coders (DAE) is a new approach in deep learning used in image denoising that has a challenging performance. In this study, DAE technique is applied on 2-DGE images motivated by its ability to learn a robust representation to partially corrupted input. DAE is applied on over than 300 real gels got from LEeB 2-D PAGE database. To validate the efficiency of this technique three indicators are used; Signal-to-noise ratio (SNR), False discovery rate (FDR) and spot efficiency. The average results before denoising are 0.6332 for SNR and 71.05 for spot efficiency. Whereas, the average results after DAE are 61.3317 for SNR, 99.9944 for FDR and 88.4 for spot efficiency. Moreover, DAE outperforms the denoising wavelet by 1.75 %.