@Article{Madaan2021, author = {Vishu Madaan and Aditya Roy and Charu Gupta and Prateek Agrawal and Anand Sharma and Cristian Bologa and Radu Prodan}, journal = {New Generation Computing}, title = {{XCOVNet: Chest X-ray Image Classification for COVID-19 Early Detection Using Convolutional Neural Networks}}, year = {2021}, issn = {1882-7055}, month = {feb}, pages = {1--15}, abstract = {COVID-19 (also known as SARS-COV-2) pandemic has spread in the entire world. It is a contagious disease that easily spreads from one person in direct contact to another, classified by experts in five categories: asymptomatic, mild, moderate, severe, and critical. Already more than 66 million people got infected worldwide with more than 22 million active patients as of 5 December 2020 and the rate is accelerating. More than 1.5 million patients (approximately 2.5% of total reported cases) across the world lost their life. In many places, the COVID-19 detection takes place through reverse transcription polymerase chain reaction (RT-PCR) tests which may take longer than 48 h. This is one major reason of its severity and rapid spread. We propose in this paper a two-phase X-ray image classification called XCOVNet for early COVID-19 detection using convolutional neural Networks model. XCOVNet detects COVID-19 infections in chest X-ray patient images in two phases. The first phase pre-processes a dataset of 392 chest X-ray images of which half are COVID-19 positive and half are negative. The second phase trains and tunes the neural network model to achieve a 98.44% accuracy in patient classification.}, doi = {10.1007/s00354-021-00121-7}, keywords = {Coronavirus, SARS-COV-2, COVID-19 disease diagnosis, Machine learning, Image classification}, publisher = {Springer Science and Business Media LLC}, url = {https://link.springer.com/article/10.1007/s00354-021-00121-7} }