TY - GEN
T1 - An Artificial Intelligence Based Technique for COVID-19 Diagnosis from Chest X-Ray
AU - Bekhet, Saddam
AU - Hassaballah, M.
AU - Kenk, Mourad A.
AU - Hameed, Mohamed Abdel
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/24
Y1 - 2020/10/24
N2 - The COVID-19 pandemic had a catastrophic impact on world health and economic. This is attributed to the unavoidable delay in the diagnosis process, due to limitation of COVID-19 test kits. Thus, it is urgently required to establish more cheap and affordable diagnostic approaches. Chest X-ray is an important initial step towards a successful COVID-19 diagnose, where it is easily to detect any chest abnormalities (e.g., lung inflammation). Furthermore, majority of hospitals have X-ray devices that can be used in early COVID-19 diagnosis. However, the shortage of radiologists is a key factor that limits early COVID-19 diagnosis and negatively affects the treatment process. This paper presents an artificial intelligence based technique for early COVID-19 diagnosis from chest X-ray images using medical knowledge and deep Convolutional Neural Networks (CNNs). To this end, a deep learning model is built carefully and fine-tuned to achieve the maximum performance in COVID-19 detection. Experimental results on recent benchmark datasets demonstrate the superior performance of the proposed technique in identifying COVID-19 with 96% accuracy.
AB - The COVID-19 pandemic had a catastrophic impact on world health and economic. This is attributed to the unavoidable delay in the diagnosis process, due to limitation of COVID-19 test kits. Thus, it is urgently required to establish more cheap and affordable diagnostic approaches. Chest X-ray is an important initial step towards a successful COVID-19 diagnose, where it is easily to detect any chest abnormalities (e.g., lung inflammation). Furthermore, majority of hospitals have X-ray devices that can be used in early COVID-19 diagnosis. However, the shortage of radiologists is a key factor that limits early COVID-19 diagnosis and negatively affects the treatment process. This paper presents an artificial intelligence based technique for early COVID-19 diagnosis from chest X-ray images using medical knowledge and deep Convolutional Neural Networks (CNNs). To this end, a deep learning model is built carefully and fine-tuned to achieve the maximum performance in COVID-19 detection. Experimental results on recent benchmark datasets demonstrate the superior performance of the proposed technique in identifying COVID-19 with 96% accuracy.
KW - Artificial Intelligence
KW - Chest X-ray
KW - Convolutional Neural Networks
KW - COVID-19
KW - Deep Learning
KW - Pneumonia
UR - http://www.scopus.com/inward/record.url?scp=85097952549&partnerID=8YFLogxK
U2 - 10.1109/NILES50944.2020.9257930
DO - 10.1109/NILES50944.2020.9257930
M3 - Conference contribution
AN - SCOPUS:85097952549
T3 - 2nd Novel Intelligent and Leading Emerging Sciences Conference, NILES 2020
SP - 191
EP - 195
BT - 2nd Novel Intelligent and Leading Emerging Sciences Conference, NILES 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd Novel Intelligent and Leading Emerging Sciences Conference, NILES 2020
Y2 - 24 October 2020 through 26 October 2020
ER -
