In order to guide illness diagnosis and therapy, ultrasound imaging is essential in today's clinics. Obtaining high-quality ultrasound pictures for clinical use at the lowest possible cost and patient risk is the main goal of ultrasound image reconstruction, one of the most essential and crucial aspects of ultrasound imaging. In ultrasound image reconstruction, or more broadly in computer vision picture restoration, mathematical models have been heavily used. Earlier mathematical models—which we will refer to as handmade models—were primarily created using human knowledge or conjecture about the picture that needed to be recreated. Later, data-driven plus handmade modelling began to take shape, while it still largely depends on human designs; some of the model's knowledge is derived from the observed data. Recently, deep learning-based models, also known as deep models, have pushed data-driven modelling to the limit where the models are mostly dependent on learning with little to no human design, thanks to the increased availability of data and computing power. There are benefits and drawbacks to both data-driven and handmade modelling. Though they may not be adaptable and smart enough to fully use huge data sets, typical handmade models are easily interpreted and have strong theoretical underpinnings for robustness, recoverability, complexity, etc. On the other hand, while they still lack theoretical underpinnings, data-driven models—especially deep models—are often much more adaptable and successful in obtaining valuable information from massive data sets. In order to reap the advantages of both methods, combining deep modelling with handmade modelling is one of the main research topics in medical imaging. This article primarily presents a conceptual assessment of some recent research on deep modelling from the perspective of unrolling dynamics. From this perspective, new neural network architectural ideas are stimulated, drawing inspiration from numerical differential equations and optimization methods. Despite the widespread use of deep modelling, there are still many unmet potential and problems in the subject, which we will cover in our article's conclusion.
RNN-ReLU (Recurrent Neural Network with Rectified Linear Unit), Precision, Recall, Specificity, F1-Measure, Accuracy
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