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Current diagnostic methods for coronavirus disease 2019 (COVID-19) mainly rely on reverse transcription polymerase chain reaction (RT-PCR). However, RT-PCR is costly and time)consuming. Therefore, an accurate, rapid, and inexpensive screening method must be developed for the diagnosis of COVID-19. In this study, we combined image processing technologies with deep learning algorithms to enhance the accuracy of COVID-19 identification from chest X-ray (CXR) sensor images. Contrast-limited adaptive histogram equalization (CLAHE) was used to improve the visibility level of unclear images. In addition, we examined whether our image fusion technique can effectively improve the performance of seven deep learning models (MobileNetV2, ResNet50, ResNet152V2, Inception-ResNet-v2, DenseNet121, DenseNet201, and Xception). The proposed feature fusion technique involves merging the features of an original image with those of an image subjected to CLAHE and then using the merged features to retrain, test, and validate deep learning models for identifying COVID-19 in CXR images. To avoid incidences of images not matching reality and to ensure high model stability, no data enhancement was conducted. The results of this study indicate that the proposed image fusion technique can improve the classification evaluation indicators, especially the sensitivity of deep learning models in two-class and three-class sortings. Sensitivity refers to a model’s ability to detect an infection correctly. The highest accuracy in this study was achieved when combining Xception with the proposed feature fusion technique. In three-class sorting, the accuracy of this method was 99.74%, with the average accuracy of fivefold cross-validation being 99.19%. In two-class sorting, the accuracy of the aforementioned method was 99.74%, with the average accuracy of fivefold cross-validation being 99.50%. The results showed that the proposed image processing technologies with deep learning algorithms have exceptional generalization. [ABSTRACT FROM AUTHOR]

This study proposed a measurement platform for continuous blood pressure estimation based on dual photoplethysmography (PPG) sensors and a deep learning (DL) that can be used for continuous and rapid measurement of blood pressure and analysis of cardiovascular-related indicators. The proposed platform measured the signal changes in PPG and converted them into physiological indicators, such as pulse transit time (PTT), pulse wave velocity (PWV), perfusion index (PI) and heart rate (HR); these indicators were then fed into the DL to calculate blood pressure. The hardware of the experiment comprised 2 PPG components (i.e., Raspberry Pi 3 Model B and analog-todigital converter [MCP3008]), which were connected using a serial peripheral interface. The DL algorithm converted the stable dual PPG signals acquired from the strictly standardized experimental process into various physiological indicators as input parameters and finally obtained the systolic blood pressure (SBP), diastolic blood pressure (DBP) and mean arterial pressure (MAP). To increase the robustness of the DL model, this study input data of 100 Asian participants into the training database, including those with and without cardiovascular disease, each with a proportion of approximately 50%. The experimental results revealed that the mean absolute error and standard deviation of SBP was 0.17±0.46 mmHg. The mean absolute error and standard deviation of DBP was 0.27±0.52 mmHg. The mean absolute error and standard deviation of MAP was 0.16±0.40 mmHg. [ABSTRACT FROM AUTHOR]