With the advancements in biomedical imaging applications, it becomes more important to provide potential results for searching the biomedical imaging data. During the health emergency, tremors require efficient results at rapid speed to provide results to spatial queries using the Web. An efficient biomedical search engine can obtain the significant search intention and return additional important contents in which users have already indicated some interest. The development of biomedical search engines is still an open area of research. Recently, many researchers have utilized various deep-learning models to improve the performance of biomedical search engines. However, the existing deep-learning-based biomedical search engines suffer from the overfitting and hyperparameter tuning problems. Therefore, in this paper, a nondominated-sorting-genetic-algorithm-III- (NSGA-III-) based deep-learning model is proposed for biomedical search engines. Initially, the hyperparameters of the proposed deep-learning model are obtained using the NSGA-III. Thereafter, the proposed deep-learning model is trained by using the tuned parameters. Finally, the proposed model is validated on the testing dataset. Comparative analysis reveals that the proposed model outperforms the competitive biomedical search engine models.

中文翻译：

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基于NSGA-III的生物医学搜索引擎深度学习模型

随着生物医学成像应用的进步，为搜索生物医学成像数据提供潜在的结果变得越来越重要。在突发卫生事件期间，震颤需要快速有效的结果，以便使用Web向空间查询提供结果。一个高效的生物医学搜索引擎可以获得重要的搜索意图，并返回用户已经表示有兴趣的其他重要内容。生物医学搜索引擎的开发仍是一个开放的研究领域。最近，许多研究人员已经利用各种深度学习模型来提高生物医学搜索引擎的性能。但是，现有的基于深度学习的生物医学搜索引擎存在过度拟合和超参数调整的问题。因此，在本文中，针对生物医学搜索引擎，提出了一种基于非支配排序遗传算法-III（NSGA-III-）的深度学习模型。最初，使用NSGA-III获得了所提出的深度学习模型的超参数。此后，通过使用调整后的参数对提出的深度学习模型进行训练。最后，在测试数据集上对提出的模型进行了验证。比较分析表明，所提出的模型优于竞争性生物医学搜索引擎模型。