Vieira Pablo A, Magalhães Deborah M V, Carvalho-Filho Antonio O, Veras Rodrigo M S, Rabêlo Ricardo A L, Silva Romuere R V
Electrical Engineering, Federal University of Piauí, Teresina, Brazil.
Information Systems, Federal University of Piauí, Picos, Brazil.
Comput Electr Eng. 2021 Dec;96:107467. doi: 10.1016/j.compeleceng.2021.107467. Epub 2021 Sep 24.
New and more transmissible SARS-COV-2 variants aggravated the SARS-COV-2 emergence. Lung X-ray images stand out as an alternative to support case screening. The latest computer-aided diagnosis systems have been using Deep Learning (DL) to detect pulmonary diseases. In this context, our work investigates different types of pneumonia detection, including COVID-19, based on X-ray image processing and DL techniques. Our methodology comprehends a pre-processing step including data-augmentation, contrast enhancement, and resizing method to overcome the challenge of heterogeneous and few samples of public datasets. Additionally, we propose a new Genetic Fine-Tuning method to automatically define an optimal set of hyper-parameters of ResNet50 and VGG16 architectures. Our results are encouraging; we achieve an accuracy of 97% considering three classes: COVID-19, other pneumonia, and healthy. Thus, our methodology could assist in classifying COVID-19 pneumonia, which could reduce costs by making the process faster and more efficient.
新的、更具传播性的严重急性呼吸综合征冠状病毒2（SARS-CoV-2）变种加剧了SARS-CoV-2的出现。肺部X光图像作为支持病例筛查的一种替代方法而备受关注。最新的计算机辅助诊断系统一直在使用深度学习（DL）来检测肺部疾病。在此背景下，我们的工作基于X光图像处理和DL技术，研究包括新冠肺炎在内的不同类型肺炎的检测。我们的方法包括一个预处理步骤，该步骤涵盖数据增强、对比度增强和调整大小方法，以克服公共数据集样本异质性和数量少的挑战。此外，我们提出了一种新的遗传微调方法，以自动定义ResNet50和VGG16架构的一组最优超参数。我们的结果令人鼓舞；考虑新冠肺炎、其他肺炎和健康这三类情况，我们实现了97%的准确率。因此，我们的方法可以帮助对新冠肺炎肺炎进行分类，这可以通过使过程更快、更高效来降低成本。
