Efficient anomaly detection from medical signals and images with convolutional neural networks for Internet of medical things (IoMT) systems

Deep learning is one of the most promising machine learning techniques that revolutionalized the artificial intelligence field. The known traditional and convolutional neural networks (CNNs) have been utilized in medical pattern recognition applications that depend on deep learning concepts. This is attributed to the importance of anomaly detection (AD) in automatic diagnosis systems. In this paper, the AD is performed on medical electroencephalography (EEG) signal spectrograms and medical corneal images for Internet of medical things (IoMT) systems. Deep learning based on the CNN models is employed for this task with training and testing phases. Each input image passes through a series of convolution layers with different kernel filters. For the classification task, pooling and fully‐connected layers are utilized. Computer simulation experiments reveal the success and superiority of the proposed models for automated medical diagnosis in IoMT systems. In this paper, anomaly detection (AD) is performed on medical electroencephalography (EEG) signal spectrograms and medical corneal images for Internet of Medical Things (IoMT) systems. Deep learning based on convolutional neural network models is employed in the training and testing phases. Each input image passes through a series of convolution layers with different numbers of kernel filters. For the classification task, pooling and fully‐connected layers are utilized. Computer simulation experiments reveal the superiority of the proposed models for automated medical diagnosis in IoMT systems.