Dubey Shivendra, Dubey Sakshi, Raghuwanshi Kapil, Pranjal Pranshu, Kumar Sudheer
Department of Artificial Intelligence & Machine Learning, Manipal University Jaipur, Jaipur, India.
Department of Electronics and Communications, RKDF University, Bhopal, MP, India.
Trop Dis Travel Med Vaccines. 2025 Sep 2;11(1):32. doi: 10.1186/s40794-025-00267-y.
The respiratory system of humans is impacted by infectious and deadly illnesses like COVID-19. Early identification and diagnosis of this type of illness is essential to stop the infection from spreading further. In the present research, we presented a technique for determining the condition using COVID-19's current genome sequences employing the DenseNet-16 framework. We operated a network of already trained neurons before using a transfer learning method to prepare it according to our dataset. Additionally, we preprocessed the collected information using the NearKbest interpolation approach; then, we utilized Adam Optimizer to optimize our findings. Compared with special deep learning models like ResNet-50, VGG-19, AlexNet, and VGG-16, our approach produced an accuracy of 99.18%. The model was deployed on a platform with GPU support, which greatly decreased training time. Dataset size and the requirement for further validation are two of the study's limitations, despite the encouraging results. The current research showed how a deep learning approach may be useful to categorize the genome sequence of infectious disease like COVID-19 using the suggested GenoDense-Net architecture. The next step in this research project is conducting investigations in the clinic.
人类的呼吸系统会受到诸如新冠病毒肺炎（COVID-19）等传染性致命疾病的影响。尽早识别和诊断这类疾病对于防止感染进一步传播至关重要。在本研究中，我们提出了一种利用DenseNet-16框架，根据新冠病毒肺炎（COVID-19）当前的基因组序列来确定病情的技术。在使用迁移学习方法根据我们的数据集对其进行训练之前，我们运行了一个已经训练好的神经元网络。此外，我们使用近邻最优插值法对收集到的信息进行预处理；然后，我们利用Adam优化器来优化我们的结果。与ResNet-50、VGG-19、AlexNet和VGG-16等特殊深度学习模型相比，我们的方法准确率达到了99.18%。该模型部署在一个有GPU支持的平台上，这大大缩短了训练时间。尽管结果令人鼓舞，但数据集大小和进一步验证的需求是该研究的两个局限性。当前的研究表明，使用建议的GenoDense-Net架构，深度学习方法如何有助于对像新冠病毒肺炎（COVID-19）这样的传染病基因组序列进行分类。该研究项目的下一步是在临床中进行调查。
