Identifying COVID-19 Presence based on CT-SCAN Images

This research study explores the use of deep learning algorithms, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to identify the presenceof COVID-19 in patients using CT-scan images of the lungs. The proposed model is trained on a dataset comprising CT scans of COVID-19 positive and negative patients and achieves high accuracy and sensitivity in detecting the virus. The CNN component of the model is used to extract relevant features from the CT images, while the RNN component leverages the temporal relationships between sequential images. Additionally, we compare the performance of our deep learning model with that of traditional machine learning methods and demonstrate that the proposed approach outperforms them. The results suggest that the use of deep learning algorithms in COVID-19 detection can potentially aid in the timely diagnosis and effective management of the pandemic.