MS-NADNet: A Multi-Stream Noise-Aware Deep Learning Framework for Microarray Image Denoising
Abstract
Microarray imaging is a critical tool for large-scale gene expression analysis, yet its accuracy is oftencompromised by noise introduced during sample preparation, hybridization, and image acquisition. Traditionaldenoising approaches, including median, Wiener, and wavelet-based filtering, either rely on fixedparameters or degrade under mixed-noise conditions, while CNN-based methods treat all noise uniformly,limiting adaptability. To address these limitations, we propose MS-NADNet (Multi-Stream Noise-AwareDenoising Network), a deep-learning framework that integrates a Noise Characterization Module (NCM)to identify noise type, a set of Noise-Specific Denoising Modules (NSDMs) specialized for distinct noisedistributions, and a Global Refinement Block for residual suppression. A large-scale augmented datasetwas constructed from the Malignant Lymphoma Classification dataset, incorporating 17 noise variants, includingGaussian, Poisson, salt-and-pepper, speckle, and mixed combinations, to simulate realistic imagingconditions. Experimental evaluation demonstrates that MS-NADNet achieves an MSE of 0.00012, PSNR of42.73 dB, and SSIM of 0.9861, outperforming classical filters and state-of-the-art CNN denoisers. Theseresults confirm the robustness of MS-NADNet in handling diverse single and multi-noise environments,ensuring biologically reliable microarray image analysis and improved downstream gene expression profiling.References
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