An Artificial Intelligence Based Technique for COVID-19 Diagnosis from Chest X-Ray

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.

[1]  Wenyu Liu,et al.  Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label , 2020, medRxiv.

[2]  Bin Yang,et al.  MedGAN: Medical Image Translation using GANs , 2018, Comput. Medical Imaging Graph..

[3]  P. Munroe,et al.  Artificial intelligence and machine learning to fight COVID-19 , 2020, Physiological genomics.

[4]  Iñigo Barandiaran,et al.  COVID-19 Detection in Chest X-ray Images using a Deep Learning Approach , 2020, Int. J. Interact. Multim. Artif. Intell..

[5]  Tao Han,et al.  An effective approach for CT lung segmentation using mask region-based convolutional neural networks , 2020, Artif. Intell. Medicine.

[6]  I. Astuti,et al.  Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2): An overview of viral structure and host response , 2020, Diabetes & Metabolic Syndrome: Clinical Research & Reviews.

[7]  Yuedong Yang,et al.  Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images , 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[8]  G. Remuzzi,et al.  COVID-19 and Italy: what next? , 2020, The Lancet.

[9]  Xiang Xie,et al.  COVID-19 and the cardiovascular system , 2020, Nature Reviews Cardiology.

[10]  Vladimir Milián Núñez,et al.  Progress in Artificial Intelligence and Pattern Recognition , 2018, Lecture Notes in Computer Science.

[11]  Oscar Camacho-Nieto,et al.  A Transfer Learning Method for Pneumonia Classification and Visualization , 2020, Applied Sciences.

[12]  Y. Hu,et al.  Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China , 2020, The Lancet.

[13]  Mahmoud Hassaballah,et al.  A novel hybrid Harris hawks optimization and support vector machines for drug design and discovery , 2020, Comput. Chem. Eng..

[14]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[15]  Alexander Wong,et al.  COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest Radiography Images , 2020, ArXiv.

[16]  Aleksander Madry,et al.  Exploring the Landscape of Spatial Robustness , 2017, ICML.

[17]  Yicheng Fang,et al.  Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR , 2020, Radiology.

[18]  Saddam Bekhet,et al.  Evaluation of similarity measures for video retrieval , 2019, Multimedia Tools and Applications.

[19]  Amr Ahmed,et al.  An Integrated Signature-Based Framework for Efficient Visual Similarity Detection and Measurement in Video Shots , 2018, ACM Trans. Inf. Syst..

[20]  Alexander Wong,et al.  COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images , 2020, Scientific reports.

[21]  Jürgen Schmidhuber,et al.  Multi-column deep neural networks for image classification , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  L. Rudnicka,et al.  Artificial intelligence in diagnosis and management of COVID‐19 in dermatology , 2020, Dermatologic therapy.

[23]  Ioannis D. Apostolopoulos,et al.  Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks , 2020, Physical and Engineering Sciences in Medicine.

[24]  Ali Ismail Awad,et al.  Deep Learning in Computer Vision: Principles and Applications , 2020 .

[25]  Phillip De Leon,et al.  Falls Risk Classification of Older Adults Using Deep Neural Networks and Transfer Learning , 2020, IEEE Journal of Biomedical and Health Informatics.

[26]  W. Liang,et al.  Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography , 2020, Cell.

[27]  Joseph Paul Cohen,et al.  COVID-19 Image Data Collection , 2020, ArXiv.

[28]  Jun Liu,et al.  Relation Between Chest CT Findings and Clinical Conditions of Coronavirus Disease (COVID-19) Pneumonia: A Multicenter Study. , 2020, AJR. American journal of roentgenology.