Deep convolutional neural networks improve vein image quality

Abstract

Analysis of near-infrared images has a possibility to simply find vein disease. If super-resolution (SR) techniques improve the quality of near-infrared images with a low signal-to-noise ratio, they could detect abnormal veins at an early stage. Deep convolutional neural networks (DCNNs) as a SR technique were applied to downgraded images, and the effectiveness was investigated. The DCNNs with the optimal structure and parameters improved the vein image quality, although the target images contained complicated shapes of veins. Furthermore, the DCNN may estimate the hidden information on actual veins. In future, creating of an e-healthcare system with SR techniques will be useful for the quick evaluation of abnormal veins.