FuzH-PID: Highly controllable and stable DNN for COVID-19 detection via improved stochastic optimization

Amid the ongoing pandemic, reducing reliance on manual diagnostic procedures has become crucial. In this light, deep neural networks (DNNs) have demonstrated substantial progress in coronavirus disease 2019 (COVID-19) detection. However, when exposed to ‘toxic samples’—imprecise or uncertain data such as outliers, noisy or mislabeled entries, that negatively impact the training process—existing methods cannot effectively protect the training convergence from the overshoot phenomenon. This situation would slow the training convergence. Additionally, current diagnostic models necessitate substantial re-tuning time to adapt to new virus strains or to handle data from different platforms. This research focuses on the parameter updates design and propose a highly controllable and stable DNN for COVID-19 detection. By exploiting the past, current and future changes of the gradient in a fuzzy logic manner, and taking into account the cross-coupling effect between the gradient and its rate of change, we achieve dynamic, high-precision control on parameter updates in DNN optimization to reach a stable status at a faster convergence rate. In each iteration, the current learning rate adjusts itself to the current optimal value within the fuzzy neighboring region. Potentially hereditary module sequentially transfers the trained knowledge between estimators while updating the fuzzy universe range based on the calculated contraction–expansion factors. Consequently, our proposed algorithm alleviates the overshoot suffered by toxic samples, meanwhile effectively enhancing the model robustness, resource-efficiency, flexibility, adaptability, and compatibility. When tested on popular DNN architectures, it yields up to 47.18% acceleration with promising accuracy on four public datasets. Extensive experiments prove the effectiveness of our method in comparison to state-of-the-art optimizers and diagnosis systems, facilitating the real-life demands for COVID-19 detection.