ABSTRACT
This work presents a novel solution for the detection of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARSCoV-2) that produces the disease COVID-19 from gel electrophoresis images for the application of the analysis of samples from sewage systems for the control of the pandemic, using the fifth generation mobile network (5G) and artificial intelligence (AI) for the reduction of noise in the samples and the detection of characteristic bands of the virus. It is composed of five steps, allowing to reduce the fatigue of the experts and the cost to perform the SARS-CoV-2 detection process compared to an RT-qPCR. In terms of energy savings in transmission, a gain was achieved that stops the value of a BER of 10-4, it is 0.5 dB when going from N from 64 to 128, similar for the difference from 32 to 64 and a gain of 1dB when going from N from 16 to 32, respectively. © 2022 IEEE.
ABSTRACT
Due to the coronavirus pandemic and the lack of an automatic COVID-19 diagnostic system to relieve congestion in health centers and to support the traceability of this disease, this article exposes the implementation of algorithms for automatic diagnosis of lung diseases such as COVID-19 and Pneumonia from chest X-rays (CXR) through GLCM and HOG features extraction using 6300 patches. Then, selecting the best features and different classifiers such as an Support Vector Machine (SVM) and Artificial Neural Network (ANN) to obtain a system maximum accuracy of 93,73% for SVM. © 2021 IEEE.