tetano
Editor, Senior Moderator
Appl Soft Comput
. 2022 Jan;115:108190.
doi: 10.1016/j.asoc.2021.108190. Epub 2021 Dec 5.
Fully automatic deep convolutional approaches for the analysis of COVID-19 using chest X-ray images
Joaquim de Moura[SUP] 1 2 [/SUP], Jorge Novo[SUP] 1 2 [/SUP], Marcos Ortega[SUP] 1 2 [/SUP]
Affiliations
Abstract
Covid-19 is a new infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Given the seriousness of the situation, the World Health Organization declared a global pandemic as the Covid-19 rapidly around the world. Among its applications, chest X-ray images are frequently used for an early diagnostic/screening of Covid-19 disease, given the frequent pulmonary impact in the patients, critical issue to prevent further complications caused by this highly infectious disease. In this work, we propose 4 fully automatic approaches for the classification of chest X-ray images under the analysis of 3 different categories: Covid-19, pneumonia and healthy cases. Given the similarity between the pathological impact in the lungs between Covid-19 and pneumonia, mainly during the initial stages of both lung diseases, we performed an exhaustive study of differentiation considering different pathological scenarios. To address these classification tasks, we evaluated 6 representative state-of-the-art deep network architectures on 3 different public datasets: (I) Chest X-ray dataset of the Radiological Society of North America (RSNA); (II) Covid-19 Image Data Collection; (III) SIRM dataset of the Italian Society of Medical Radiology. To validate the designed approaches, several representative experiments were performed using 6,070 chest X-ray radiographs. In general, satisfactory results were obtained from the designed approaches, reaching a global accuracy values of 0.9706 ±
0.0044, 0.9839 ± 0.0102, 0.9744 ± 0.0104 and 0.9744 ±
0.0104, respectively, thus helping the work of clinicians in the diagnosis and consequently in the early treatment of this relevant pandemic pathology.
Keywords: Computer-aided diagnosis; Covid-19; Deep learning; Pneumonia; Pulmonary disease detection; X-ray imaging.
. 2022 Jan;115:108190.
doi: 10.1016/j.asoc.2021.108190. Epub 2021 Dec 5.
Fully automatic deep convolutional approaches for the analysis of COVID-19 using chest X-ray images
Joaquim de Moura[SUP] 1 2 [/SUP], Jorge Novo[SUP] 1 2 [/SUP], Marcos Ortega[SUP] 1 2 [/SUP]
Affiliations
- PMID: 34899109
- PMCID: PMC8645263
- DOI: 10.1016/j.asoc.2021.108190
Abstract
Covid-19 is a new infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Given the seriousness of the situation, the World Health Organization declared a global pandemic as the Covid-19 rapidly around the world. Among its applications, chest X-ray images are frequently used for an early diagnostic/screening of Covid-19 disease, given the frequent pulmonary impact in the patients, critical issue to prevent further complications caused by this highly infectious disease. In this work, we propose 4 fully automatic approaches for the classification of chest X-ray images under the analysis of 3 different categories: Covid-19, pneumonia and healthy cases. Given the similarity between the pathological impact in the lungs between Covid-19 and pneumonia, mainly during the initial stages of both lung diseases, we performed an exhaustive study of differentiation considering different pathological scenarios. To address these classification tasks, we evaluated 6 representative state-of-the-art deep network architectures on 3 different public datasets: (I) Chest X-ray dataset of the Radiological Society of North America (RSNA); (II) Covid-19 Image Data Collection; (III) SIRM dataset of the Italian Society of Medical Radiology. To validate the designed approaches, several representative experiments were performed using 6,070 chest X-ray radiographs. In general, satisfactory results were obtained from the designed approaches, reaching a global accuracy values of 0.9706 ±
0.0044, 0.9839 ± 0.0102, 0.9744 ± 0.0104 and 0.9744 ±
0.0104, respectively, thus helping the work of clinicians in the diagnosis and consequently in the early treatment of this relevant pandemic pathology.
Keywords: Computer-aided diagnosis; Covid-19; Deep learning; Pneumonia; Pulmonary disease detection; X-ray imaging.