Determination of the Severity and Percentage of COVID-19 Infection through a Hierarchical Deep Learning System
Metadatos
Mostrar el registro completo del ítemAutor
Ortiz, Sergio; Rojas Ruiz, Fernando José; Valenzuela Cansino, Olga; Herrera Maldonado, Luis Javier; Rojas Ruiz, IgnacioEditorial
MDPI
Materia
Hierarchical intelligent system Deep learning COVID-19 Support vector machine
Fecha
2022-03-28Referencia bibliográfica
Ortiz, S... [et al.]. Determination of the Severity and Percentage of COVID-19 Infection through a Hierarchical Deep Learning System. J. Pers. Med. 2022, 12, 535. [https://doi.org/10.3390/jpm12040535]
Patrocinador
Spanish Government RTI-2018-101674-B-I00; Junta de Andalucia CV20-64934 P20-00163Resumen
The coronavirus disease 2019 (COVID-19) has caused millions of deaths and one of the
greatest health crises of all time. In this disease, one of the most important aspects is the early
detection of the infection to avoid the spread. In addition to this, it is essential to know how the
disease progresses in patients, to improve patient care. This contribution presents a novel method
based on a hierarchical intelligent system, that analyzes the application of deep learning models to
detect and classify patients with COVID-19 using both X-ray and chest computed tomography (CT).
The methodology was divided into three phases, the first being the detection of whether or not a
patient suffers from COVID-19, the second step being the evaluation of the percentage of infection of
this disease and the final phase is to classify the patients according to their severity. Stratification
of patients suffering from COVID-19 according to their severity using automatic systems based on
machine learning on medical images (especially X-ray and CT of the lungs) provides a powerful
tool to help medical experts in decision making. In this article, a new contribution is made to a
stratification system with three severity levels (mild, moderate and severe) using a novel histogram
database (which defines how the infection is in the different CT slices for a patient suffering from
COVID-19). The first two phases use CNN Densenet-161 pre-trained models, and the last uses
SVM with LDA supervised learning algorithms as classification models. The initial stage detects the
presence of COVID-19 through X-ray multi-class (COVID-19 vs. No-Findings vs. Pneumonia) and
the results obtained for accuracy, precision, recall, and F1-score values are 88%, 91%, 87%, and 89%,
respectively. The following stage manifested the percentage of COVID-19 infection in the slices of
the CT-scans for a patient and the results in the metrics evaluation are 0.95 in Pearson Correlation
coefficient, 5.14 in MAE and 8.47 in RMSE. The last stage finally classifies a patient in three degrees of
severity as a function of global infection of the lungs and the results achieved are 95% accurate.