Computer-aided diagnosis based on intelligent systems is an effective strategy to improve the efficiency of healthcare systems while reducing their costs. In this work, the epilepsy detection task is approached in two different ways, recurrent and convolutional neural networks, within a patient-specific scheme. Additionally, a detector function and its effects on seizure detection performance are presented. Our results suggest that it is possible to detect seizures from scalp EEGs with acceptable results for some patients, and that the DeepHealth framework is a proper deep learning software for medical research.
Medical diagnosis assisted by intelligent systems is an effective strategy to increase the efficiency of healthcare systems while reducing their costs. This work is focused on detecting pulmonary conditions from X-ray images using the DeepHealth framework. Our results suggest that it is possible to discriminate pulmonary conditions compatible with the COVID-19 disease from other conditions and healthy individuals. Hence, it could be stated that the DeepHealth framework is a suitable deep-learning software with which to perform reliable medical research. However, more medical data and research are still necessary to train deep learning models that could be trusted by medical personnel.
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