Advances in Computer-Aided Medical Image Processing

Abstract

Featured Application: Enhancing Clinical Diagnosis through the Integration of Deep Learning Techniques in Medical Image Recognition. This comprehensive review highlights the transformative potential of deep learning techniques in medical image recognition, with a focus on applications that can improve the accuracy and efficiency of clinical diagnosis. By examining a range of approaches, including image enhancement, multimodal medical image fusion, and intelligent image recognition tailored to specific anatomical structures, this study demonstrates the effectiveness of advanced neural network designs in extracting multilevel features from medical images. The featured application emphasizes the importance of addressing key challenges, such as data quality, model interpretability, generalizability, and computational resource requirements. By exploring 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, ultimately leading to enhanced patient care. Through this featured application, the potential of deep learning techniques to revolutionize medical imaging is brought to the forefront, demonstrating how these advanced methods can support clinicians in making more informed diagnostic decisions, ultimately improving patient outcomes and the overall quality of healthcare. 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.