en 4-Biomedical Signal & Image Processing Biomedical Signal & Image Processing Research Paper Research Paper <div style="text-align: justify;"><span style="color:#000000;"><span style="font-size:14px;"><span style="line-height:115%"><span new="" roman="" style="font-family:" times="">The coronavirus disease or COVID-19, as a global disease, is an unprecedented health care crisis due to increasing mortality and its high rate of infection. Patients usually show significant complications in the respiratory system. This disease is caused by SARS-CoV-2. Decreasing the time of diagnosis is essential for reducing deaths and low spreading of the virus. Also, using the optimal tool in the pediatric setting and Intensive care unit (ICU) is required. Therefore, using lung ultrasound is recommended. It does not have any radiation and it has a lower cost. However, it makes noisy and low-quality data. In this paper, we propose a novel approach called Uniform Local Binary Pattern on Five intersecting Planes and convolutional neural Network (ULBPFP-Net) that overcomes the said limitation. We extract worthwhile features from five planes for feeding a network. Our experiments confirm the success of the ULBPFP-Net in COVID-19 diagnosis compared to the previous approaches.</span></span></span></span></div> COVID-19, Convolutional Neural Network, ULBPFP-Net, Lung Ultrasound Images. 14 22 http://ijeee.iust.ac.ir/browse.php?a_code=A-10-2890-1&slc_lang=en&sid=1 V. Esmaeili V. Esmaeili v.esmaeili@tabrizu.ac.ir 1800319475328460012742 1800319475328460012742 No Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran M. Mohassel Feghhi M. Mohassel Feghhi mohasselfeghhi@tabrizu.ac.ir 1800319475328460012743 000000027193843X Yes Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran