Pulmonary Diffuse Airspace Opacities Diagnosis from Chest X-Ray Images Using Deep Convolutional Neural Networks Fine-Tuned by Whale Optimizer.

The early diagnosis and the accurate separation of COVID-19 from non-COVID-19 cases based on pulmonary diffuse airspace opacities is one of the challenges facing researchers. Recently, researchers try to exploit the Deep Learning (DL) method's capability to assist clinicians and radiologists in diagnosing positive COVID-19 cases from chest X-ray images. In this approach, DL models, especially Deep Convolutional Neural Networks (DCNN), propose real-time, automated effective models to detect COVID-19 cases. However, conventional DCNNs usually use Gradient Descent-based approaches for training fully connected layers. Although GD-based Training (GBT) methods are easy to implement and fast in the process, they demand numerous manual parameter tuning to make them optimal. Besides, the GBT's procedure is inherently sequential, thereby parallelizing them with Graphics Processing Units is very difficult. Therefore, for the sake of having a real-time COVID-19 detector with parallel implementation capability, this paper proposes the use of the Whale Optimization Algorithm for training fully connected layers. The designed detector is then benchmarked on a verified dataset called COVID-Xray-5k, and the results are verified by a comparative study with classic DCNN, DUICM, and Matched Subspace classifier with Adaptive Dictionaries. The results show that the proposed model with an average accuracy of 99.06% provides 1.87% better performance than the best comparison model. The paper also considers the concept of Class Activation Map to detect the regions potentially infected by the virus. This was found to correlate with clinical results, as confirmed by experts. Although results are auspicious, further investigation is needed on a larger dataset of COVID-19 images to have a more comprehensive evaluation of accuracy rates.

Establishment of an Internet-Based Epidemiological Survey Data Collection Customized System Model.

Epidemiology occupies a very important position in preventive medicine. Its essence is to summarize the etiology and epidemic laws by studying the distribution and possible effects of diseases, so as to promote the formation of scientific epidemic prevention measures. The purpose of this article is to help relevant personnel complete the collection, induction, and analysis of epidemiological survey data by establishing a data collection system model to improve work efficiency. This article focuses on the new coronavirus pneumonia (COVID-19), investigates the development status of epidemiological survey data collection, and analyzes the problems in the current business process, and on this basis, develops a dedicated epidemiological survey System model for data collection. From the experimental data, the optimized correction evaluation index has been increased from 8.384 to 9.067. It can be seen that the combination of data mining algorithms and backpropagation algorithms can better improve the system's ability to process information. Professional information disclosure platforms can have a good positive impression on the prevention and treatment of epidemics. The Internet-based epidemiological survey customized system model established in this article is to integrate various epidemiological data so that people can correctly understand the spread of epidemics and promote the development of preventive medicine.