Image Reconstruction using Deep Learning with Relu Activation Function for Ultrasound Imaging

Abstract

Ultrasound imaging is a widely used diagnostic tool in medical practice, but it often suffers from low resolution, noise, and missing data due to various factors such as motion artifacts and limited imaging quality. This paper presents an innovative approach to improving ultrasound image quality through deep learning techniques, specifically utilizing Convolutional Neural Networks (CNNs) with ReLU (Rectified Linear Unit) activation functions for image reconstruction. The ReLU activation function is employed to introduce non-linearity and enhance model efficiency by overcoming the vanishing gradient problem, enabling better feature learning and faster convergence during training. The deep learning model is trained on a large dataset of high-quality ultrasound images to learn the mapping from noisy or incomplete input data to high-resolution output images. The proposed method aims to fill in missing data, reduce noise, and improve overall image clarity, which is critical for accurate medical diagnosis. The results demonstrate that this deep learning-based approach with ReLU activation significantly enhances the resolution and quality of ultrasound images, making it a promising tool for medical imaging applications. The method's advantages include better image quality, noise reduction, and efficient real-time reconstruction, although challenges like data quality and model generalization across different ultrasound devices remain. This work highlights the potential of deep learning in advancing ultrasound imaging for improved patient care and diagnosis.