PSTCNN: Explainable COVID-19 diagnosis using PSO-guided self-tuning CNN
Biocell
; 47(2):373-384, 2023.
Article
in English
| Scopus | ID: covidwho-2246222
ABSTRACT
Since 2019, the coronavirus disease-19 (COVID-19) has been spreading rapidly worldwide, posing an unignorable threat to the global economy and human health. It is a disease caused by severe acute respiratory syndrome coronavirus 2, a single-stranded RNA virus of the genus Betacoronavirus. This virus is highly infectious and relies on its angiotensin-converting enzyme 2-receptor to enter cells. With the increase in the number of confirmed COVID-19 diagnoses, the difficulty of diagnosis due to the lack of global healthcare resources becomes increasingly apparent. Deep learning-based computer-aided diagnosis models with high generalisability can effectively alleviate this pressure. Hyperparameter tuning is essential in training such models and significantly impacts their final performance and training speed. However, traditional hyperparameter tuning methods are usually time-consuming and unstable. To solve this issue, we introduce Particle Swarm Optimisation to build a PSO-guided Self-Tuning Convolution Neural Network (PSTCNN), allowing the model to tune hyperparameters automatically. Therefore, the proposed approach can reduce human involvement. Also, the optimisation algorithm can select the combination of hyperparameters in a targeted manner, thus stably achieving a solution closer to the global optimum. Experimentally, the PSTCNN can obtain quite excellent results, with a sensitivity of 93.65% ± 1.86%, a specificity of 94.32% ± 2.07%, a precision of 94.30% ± 2.04%, an accuracy of 93.99% ± 1.78%, an F1-score of 93.97% ± 1.78%, Matthews Correlation Coefficient of 87.99% ± 3.56%, and Fowlkes-Mallows Index of 93.97% ± 1.78%. Our experiments demonstrate that compared to traditional methods, hyperparameter tuning of the model using an optimisation algorithm is faster and more effective. © 2023 Centro Regional de Invest. Cientif. y Tecn.. All rights reserved.
Article; computer assisted tomography; controlled study; convolutional neural network; coronavirus disease 2019; correlation coefficient; cross validation; data visualization; detection algorithm; diagnostic accuracy; diagnostic test accuracy study; F1 score; feature learning (machine learning); feature selection; female; Fowlkes Mallows Index; human; intermethod comparison; major clinical study; male; particle swarm optimization; receiver operating characteristic; scoring system; sensitivity and specificity; statistical analysis; COVID-19; Hyperparameters tuning; Particle swarm optimisation; SARS-CoV-2
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
Biocell
Year:
2023
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS