2020/05/02 by Pierre G. B. Moutounet-Cartan, Moutounet-Cartan, Pierre G. B.
Computer Science · Medicine · #Anomaly Detection Techniques and Applications #COVID-19 diagnosis using AI #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Healthcare #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2005.00845
openalex publication_date 2020/05/02 · openalex created_date 2020/05/13 · openalex updated_date 2026/07/28
The article explores different deep convolutional neural network architectures trained and tested on posteroanterior chest X-rays of 327 patients who are healthy (152 patients), diagnosed with COVID-19 (125), and other types of pneumonia (48). In particular, this paper looks at the deep convolutional neural networks VGG16 and VGG19, InceptionResNetV2 and InceptionV3, as well as Xception, all followed by a flat multi-layer perceptron and a final 30% drop-out. The paper has found that the best performing network is VGG16 with a final 30% drop-out trained over 3 classes (COVID-19, No Finding, Other Pneumonia). It has an internal cross-validated accuracy of 93.9(±3.4)%, a COVID-19 sensitivity of 87.7(-1.9,+2)%, and a No Finding sensitivity of 96.8(±0.8)%. The respective external cross-validated values are 84.1(±13.5)%, 87.7(-1.9,2)%, and 96.8(±0.8)%. The model optimizer was Adam with a 1e-4 learning rate, and categorical cross-entropy loss. It is hoped that, once this research will be put to practice in hospitals, healthcare professionals will be able in the medium to long-term to diagnosing through machine learning tools possible pneumonia, and if detected, whether it is linked to a COVID-19 infection, allowing the detection of new possible COVID-19 foyers after the end of possible "stop-and-go" lockdowns as expected by until a vaccine is found and widespread. Furthermore, in the short-term, it is hoped practitioners can compare the diagnosis from the deep convolutional neural networks with possible RT-PCR testing results, and if clashing, a Computed Tomography could be performed as they are more accurate in showing COVID-19 pneumonia.