Advances in Computer-Aided Medical Image Processing
نویسندگان
چکیده
The primary objective of this study is to provide an extensive review deep learning techniques for medical image recognition, highlighting their potential improving diagnostic accuracy and efficiency. We systematically organize the paper by first discussing characteristics challenges imaging techniques, with a particular focus on magnetic resonance (MRI) computed tomography (CT). Subsequently, we delve into direct processing methods, such as enhancement multimodal fusion, followed examination intelligent recognition approaches tailored specific anatomical structures. These employ various models including convolutional neural networks (CNNs), transfer learning, attention mechanisms, cascading strategies, overcome related unclear edges, overlapping regions, structural distortions. Furthermore, emphasize significance network design in imaging, concentrating extraction multilevel features using U-shaped structures, dense connections, 3D convolution, feature fusion. Finally, identify address key data quality, model interpretability, generalizability, computational resource requirements. By proposing future directions accessibility, active explainable AI, robustness, efficiency, paves way successful integration AI clinical practice enhanced patient care.
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ژورنال
عنوان ژورنال: Applied sciences
سال: 2023
ISSN: ['2076-3417']
DOI: https://doi.org/10.3390/app13127079