Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays
By Luca Brunese, Francesco Mercaldo, Alfonso Reginelli, Antonella Santone. Comput. Methods Programs Biomed., 2020
516 citations recorded by the source corpus.
Background and Objective: Coronavirus disease (COVID-19) is an infectious disease caused by a new virus never identified before in humans. This virus causes respiratory disease (for instance, flu) with symptoms such as cough, fever and, in severe cases, pneumonia. The test to detect the presence of this virus in humans is performed on sputum or blood samples and the outcome is generally available within a few hours or, at most, days. Analysing biomedical imaging the patient shows signs of pneumonia. In this paper, with the aim of providing a fully automatic and faster diagnosis, we propose the adoption of deep learning for COVID-19 detection from X-rays. Method: In particular, we propose an approach composed by three phases: the first one to detect if in a chest X-ray there is the presence of a pneumonia. The second one to discern between COVID-19 and pneumonia. The last step is aimed to localise the areas in the X-ray symptomatic of the COVID-19 presence. Results and Conclusion: Experimental analysis on 6,523 chest X-rays belonging to different institutions demonstrated the effectiveness of the proposed approach, with an average time for COVID-19 detection of approximately 2.5 seconds and an average accuracy equal to 0.97.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/4cd06c7b06e167e8494d752316f8dbc298639a3d
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Citations in the corpus
- Machine-Learning-Based Disease Diagnosis: A Comprehensive Review (2021, methodology): “For example, Brunese et al. (2020) employed 6505 images with a data ratio of 1:1.17, with 3003 images classified as COVID-19 symptoms and 3520 as "other patients" for the objectives of that study [98].”
- Machine Learning-Based Heart Disease Diagnosis: A Systematic Literature Review (2021, background): “Even though various explainable or interpretable methodologies have recently been proposed, the ML-based model still fails to provide improved explanations for the model’s trustworthiness and interpretation [58, 14, 59].”