000 02489 am a22002413u 4500
042 _adc
100 1 0 _aWang, Wei
_eauthor
700 1 0 _aPei, Yanrong
_eauthor
_91798
700 1 0 _aWang, Shui-Hua
_eauthor
700 1 0 _aGorrz, Juan manuel
_eauthor
_91800
700 1 0 _aZhang, Yu-Dong
_eauthor
245 0 0 _aPSTCNN: Explainable COVID-19 diagnosis using PSO-guided self-tuning CNN
260 _c2023.
500 _a/pmc/articles/PMC7613982/
500 _a/pubmed/36570878
520 _aSince 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.
540 _a
546 _aen
690 _aArticle
655 7 _aText
_2local
786 0 _nBiocell
856 4 1 _uhttp://dx.doi.org/10.32604/biocell.2021.0xxx
_zConnect to this object online.
999 _c2047
_d2047