Treffer: Research from Northeastern University Provides New Data on Intelligence-Based Medicine (Deep learning methods for noisy sperm image classification from convolutional neural network to visual transformer: a comprehensive comparative study).

A recent study conducted by researchers at Northeastern University in China explored the use of deep learning methods for classifying sperm images in computer-assisted sperm analysis (CASA) applications. The study aimed to investigate the anti-noise robustness of deep learning classification methods applied to sperm images, which are often affected by noise in practical CASA applications. The researchers conducted a comprehensive comparative study using convolutional neural network (CNN) and visual transformer (VT) deep learning methods and found that VT had strong robustness for classifying tiny object image datasets, such as sperm and impurities, under certain types of conventional noise and adversarial attacks. The study concluded that the robustness with regard to noise of VT methods based on global information is greater than that of CNN methods based on local information. [Extracted from the article]

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