PSTCNN: Explainable COVID-19 diagnosis using PSO-guided self-tuning CNN
Metadatos
Mostrar el registro completo del ítemEditorial
Tech Science
Materia
COVID-19 SARS-CoV-2 Particle swarm optimisation Convolutional neural network Hyperparameters tuning
Fecha
2022-11-18Referencia bibliográfica
WANG, W... [et al.] (2023). PSTCNN: Explainable COVID-19 diagnosis using PSO-guided self-tuning CNN. BIOCELL, 47(2), 373–384. Doi: [10.32604/biocell.2023.025905]
Patrocinador
Medical Research Council Confidence in Concept Award, UK MC_PC_17171; Royal Society International Exchanges Cost Share Award, UK RP202G0230; British Heart Foundation Accelerator Award, UK AA\18\3\34220; Hope Foundation for Cancer Research, UK RM60G0680; Global Challenges Research Fund (GCRF), UK P202PF11; Sino-UK Industrial Fund, UK RP202G0289; LIAS Pioneering Partnerships Award, UK P202ED10; Data Science Enhancement Fund, UK P202RE237; Guangxi Key Laboratory of Trusted Software, CN kx201901Resumen
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.