TY - EJOU AU - Wang, Jiaji AU - Chen, Shuwen AU - Cao, Yu AU - Zhu, Huisheng AU - Lima, Dimas TI - COVID-19 Detection Based on 6-Layered Explainable Customized Convolutional Neural Network T2 - Computer Modeling in Engineering \& Sciences PY - 2023 VL - 136 IS - 3 SN - 1526-1506 AB - This paper presents a 6-layer customized convolutional neural network model (6L-CNN) to rapidly screen out patients with COVID-19 infection in chest CT images. This model can effectively detect whether the target CT image contains images of pneumonia lesions. In this method, 6L-CNN was trained as a binary classifier using the dataset containing CT images of the lung with and without pneumonia as a sample. The results show that the model improves the accuracy of screening out COVID-19 patients. Compared to other methods, the performance is better. In addition, the method can be extended to other similar clinical conditions. KW - COVID-19; custom convolutional neural network; medical images DO - 10.32604/cmes.2023.025804