Advances in Computer-Aided Medical Image Processing

Abstract

The primary objective of this study is to provide an extensive review of deep learning techniques for medical image recognition, highlighting their potential for improving diagnostic accuracy and efficiency. We systematically organize the paper by first discussing the characteristics and challenges of medical imaging techniques, with a particular focus on magnetic resonance imaging (MRI) and computed tomography (CT). Subsequently, we delve into direct image processing methods, such as image enhancement and multimodal medical image fusion, followed by an examination of intelligent image recognition approaches tailored to specific anatomical structures. These approaches employ various deep learning models and techniques, including convolutional neural networks (CNNs), transfer learning, attention mechanisms, and cascading strategies, to overcome challenges related to unclear edges, overlapping regions, and structural distortions. Furthermore, we emphasize the significance of neural network design in medical imaging, concentrating on the extraction of multilevel features using U-shaped structures, dense connections, 3D convolution, and multimodal feature fusion. Finally, we identify and address the key challenges in medical image recognition, such as data quality, model interpretability, generalizability, and computational resource requirements. By proposing future directions in data accessibility, active learning, explainable AI, model robustness, and computational efficiency, this study paves the way for the successful integration of AI in clinical practice and enhanced patient care.

Revolutionizing Echocardiography: A Comparative Study of Advanced AI Models for Precise Left Ventricular Segmentation

“…To elaborate, the traditional methods of echocardiogram analysis require massive involvement concerning time and expertise, where some trainees should specifically work with maximum care and accuracy to provide a reliable result [8], [9]. The increasing number of patients looking forward to cardiac care and the growing demand for these professionals and healthcare infrastructure are immense pressures, to put it lightly [10], [11].…”

“…Recent advancements in artificial intelligence, particularly Convolutional Neural Networks (CNNs), refer to a very dynamic revolutionizing of the paradigms in medical imaging and diagnostics [9], [10], [11], [12]. CNNs place only remarkably fewer operational strains on the medical staff due to the inherent capability of fine recognition of image patterns, which contributes to improved diagnostic accuracy and far surpassing it [13].…”