article · International Journal for Numerical Methods in Biomedical Engineering
Deep learning techniques, particularly convolutional neural networks, play an important role in automatic diagnosis systems by identifying patterns and anomalies. This research examines anomaly detection tailored for Internet of medical things systems by analysing two distinct data types: medical electroencephalography signal spectrograms and medical corneal images. The utilised models process these inputs through structured training and testing phases. Each input image passes through multiple convolutional layers equipped with various kernel filters, followed by pooling and fully-connected layers to execute the final classification. Through computer simulation experiments, the models demonstrated success and superiority in accurately detecting anomalies for automated medical diagnosis.
Connected medical systems increasingly rely on automated tools to interpret patient health data quickly. Developing reliable deep learning methods that identify abnormalities in signals like brain activity and eye images can support faster diagnostic decisions and reduce the burden on healthcare practitioners.
The technology could enable automated diagnostic features within connected healthcare devices and IoMT platforms. Potential users include medical software providers and clinical diagnostic teams. Based on the abstract, the research has been evaluated via computer simulation experiments, indicating it is at an applied experimental stage rather than near-market deployment.
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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.
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DOI: 10.1002/cnm.3530
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