Recent Applications of Convolutional Neural Networks in Medical Data Analysis

Abstract

Cutting-edge artificial intelligence techniques especially deep learning algorithms have shown great potentials in data-driven diagnostics. Convolutional neural networks (CNNs) have been widely applied in image analysis, pattern recognition, and anomaly detection. CNNs can automatically learn features from images, avoiding human bias and improving the efficiency. The multi-layer deep network structure enables CNN to extract features at different abstraction levels in images, enhancing semantic information in images and improving its performance in various tasks such as classification, segmentation, and detection. CNN exhibits great potentials in the diagnosis, prognosis and classification of various diseases. Whereas, there are some unmet challenges in data quality and quantity, data security and privacy, model interpretability, and ethical considerations. This chapter summarizes the advantages and challenges of the state of the art, and future directions under the context of healthcare 5.0, providing a reference for clinical researchers, data scientists, and biomedical engineers.

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