Denoising of Ultrasound Images Based on Deep Learning

This paper presents a novel deep learning approach for enhancing the quality of ultrasound images, a critical tool in medical diagnostics. We have developed a Keras-based model that effectively reduces speckle noise in ultrasound images while preserving vital structural details. The model employs an encoder-decoder architecture within the TensorFlow framework, learning to distinguish and eliminate Gaussian noise introduced into a dataset of ultrasound images. A comparative analysis is conducted to evaluate the model’s performance against traditional denoising techniques, highlighting its efficacy and accuracy. Moreover, the paper extends beyond theoretical development, encompassing the creation of a user-friendly web application. This application allows healthcare professionals to effortlessly upload and assess ultrasound images, providing them with clearer, denoised outputs. The significance of work presented in this paper lies in its potential to enhance diagnostic accuracy in clinical practice, thereby contributing to improved patient care through advanced deep-learning methodologies.