Eduardo Luz

TL;DR: This work surveys the current state-of-the-art methods of ECG-based automated abnormalities heartbeat classification by presenting the ECG signal preprocessing, the heartbeat segmentation techniques, the feature description methods and the learning algorithms used.

TL;DR: A new family of models based on the EfficientNet family of deep artificial neural networks which are known for their high accuracy and low footprints is proposed, which is a promising candidate for embedding in medical equipment or even physicians’ mobile phones.

TL;DR: The results highlighted that the methods that aim at COVID-19 detection in CT-images have to improve significantly to be considered as a clinical option and larger and more diverse datasets are needed to evaluate the methods in a realistic scenario.

TL;DR: Aiming to made a fast and accurate cardiac arrhythmia signal classification process, a recent and robust supervised graph-based pattern recognition technique, the optimum-path forest (OPF) classifier is applied and analyzed.

TL;DR: In this paper, a new family of models based on the EfficientNet family of deep artificial neural networks was proposed for detecting COVID-19 in chest X-ray images, which are known for their high accuracy and low footprints.