A wavelet and local binary pattern-based feature descriptor for the detection of chronic infection through thoracic X-ray images.

This investigation attempts to propose a novel Wavelet and Local Binary Pattern-based Xception feature Descriptor (WLBPXD) framework, which uses a deep-learning model for classifying chronic infection amongst other infections. Chronic infection (COVID-19 in this study) is identified via RT-PCR test, which is time-consuming and requires a dedicated laboratory (materials, equipment, etc.) to complete the clinical results.

Wavelet and deep learning-based detection of SARS-nCoV from thoracic X-ray images for rapid and efficient testing.

This paper proposes a wavelet and artificial intelligence-enabled rapid and efficient testing procedure for patients with Severe Acute Respiratory Coronavirus Syndrome (SARS-nCoV) through a deep learning approach from thoracic X-ray images. Presently, the virus infection is diagnosed primarily by a process called the real-time Reverse Transcriptase-Polymerase Chain Reaction (rRT-PCR) based on its genetic prints. This whole procedure takes a substantial amount of time to identify and diagnose the patients infected by the virus.