Application of Convolutional Neural Networks in Bioimaging and Biosignal Processes

Dear Colleagues,

As is widely known, with the advancement of artificial intelligence (AI), the use of computer-aided diagnosis (CAD) systems in medicine has skyrocketed in recent years. Convolution neural network (CNN) models have demonstrated significantly high performance in identification, division, and classification, allowing them to be useful and effective in disease diagnosis and treatment. However, the majority of studies have used deep convolutional architectures with no significant changes. In this Special Issue, submissions on the following areas are of special interest: efforts to provide new architectures related to convolutional neural networks, new methods in feature selection/extraction and learning, and solving problems related to the scarcity and imbalance of medical data (including transfer learning, artificial data generation, and data augmentation). This research can include applications such as the automatic detection of sleep stages, the detection and classification of epilepsy stages, the automatic detection of driver fatigue, the detection and classification of emotions, etc., from physiological signals. In addition, research on the segmentation of medical images based on deep convolutional networks, including diagnosing and classifying tumors of the brain, liver, bone, etc., is also welcome.

Dr. Sebelan Danishvar
Dr. Miguel Valencia
Guest Editors

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