Feature-Guided CNN for Denoising Images From Portable Ultrasound Devices

As a non-invasive medical imaging scanning device, ultrasound has greatly increased the efficiency and accuracy of medical diagnosis. In recent years, portable ultrasound is being more widely used for its convenience and lower cost. Patients and physicians can receive the scanned images on their mobile phones at any time via a wireless network with low latency. However, it is difficult for portable ultrasound devices to capture images with the same quality as standard hospital ultrasound image acquisition systems. Usually, the images captured by portable ultrasound equipment have considerable noise. This noise undoubtedly affects the diagnosis of the physician. It is imperative to develop methods to remove the noise while preserving important information in the image. For this reason, we propose a novel denoising neural network model, called Feature-guided Denoising Convolutional Neural Network (FDCNN), to remove noise while retaining important feature information. In order to achieve high-quality denoising results, we employ a hierarchical denoising framework driven by a feature masking layer for medical images. Furthermore, we propose a feature extraction algorithm based on Explainable Artificial Intelligence (XAI) for medical images. Experimental results show that our medical image feature extraction method outperforms previous methods. Combined with the new denoising neural network architecture, portable ultrasound devices can now achieve better diagnostic performance.