Chihaoui Mejda, Dhibi Naziha, Ferchichi Ahlem
Computer Science Department, Applied College, University of Ha'il, Hail, Saudi Arabia.
REGIM: Research Groups on Intelligent Machines, University of Sfax, National School of Engineers (ENIS), Sfax, Tunisia.
Front Med (Lausanne). 2024 Dec 3;11:1498403. doi: 10.3389/fmed.2024.1498403. eCollection 2024.
Pneumonia is still a major global health issue, so effective diagnostic methods are needed. This research proposes a new methodology for improving convolutional neural networks (CNNs) and the Visual Geometry Group-16 (VGG16) model by incorporating genetic algorithms (GAs) to detect pneumonia. The work uses a dataset of 5,856 frontal chest radiography images critical in training and testing machine learning algorithms. The issue relates to challenges of medical image classification, the complexity of which can be significantly addressed by properly optimizing CNN. Moreover, our proposed methodology used GAs to determine the hyperparameters for CNNs and VGG16 and fine-tune the architecture to improve the existing performance measures. The evaluation of the optimized models showed some good performances with purely convolutional neural network archetyping, averaging 97% in terms of training accuracy and 94% based on the testing process. At the same time, it has a low error rate of 0.072. Although adding this layer affected the training and testing time, it created a new impression on the test accuracy and training accuracy of the VGG16 model, with 90.90% training accuracy, 90.90%, and a loss of 0.11. Future work will involve contributing more examples so that a richer database of radiographic images is attained, optimizing the GA parameters even more, and pursuing the use of ensemble applications so that the diagnosis capability is heightened. Apart from emphasizing the contribution of GAs in improving the CNN architecture, this study also seeks to contribute to the early detection of pneumonia to minimize the complications faced by patients, especially children.
肺炎仍然是一个重大的全球健康问题，因此需要有效的诊断方法。本研究提出了一种新方法，通过结合遗传算法（GA）来改进卷积神经网络（CNN）和视觉几何组16（VGG16）模型，以检测肺炎。这项工作使用了一个包含5856张胸部正位X光图像的数据集，该数据集对训练和测试机器学习算法至关重要。该问题涉及医学图像分类的挑战，通过适当优化CNN可以显著解决其复杂性。此外，我们提出的方法使用GA来确定CNN和VGG16的超参数，并对架构进行微调，以改善现有的性能指标。对优化模型的评估显示，在纯卷积神经网络原型设计方面有一些良好的性能，训练准确率平均为97%，测试过程中的准确率为94%。同时，其错误率较低，为0.072。虽然添加这一层影响了训练和测试时间，但它给VGG16模型的测试准确率和训练准确率带来了新的提升，训练准确率为90.90%，测试准确率为90.90%，损失为0.11。未来的工作将包括提供更多示例，以获得更丰富的X光图像数据库，进一步优化GA参数，并寻求使用集成应用程序以提高诊断能力。除了强调GA在改进CNN架构方面的贡献外，本研究还旨在为肺炎的早期检测做出贡献，以尽量减少患者尤其是儿童所面临的并发症。
