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Multi-Modal Evolutionary Deep Learning Model for Ovarian Cancer Diagnosis
Symmetry ( IF 2.940 ) Pub Date : 2021-04-10 , DOI: 10.3390/sym13040643
Rania M. Ghoniem , Abeer D. Algarni , Basel Refky , Ahmed A. Ewees

Ovarian cancer (OC) is a common reason for mortality among women. Deep learning has recently proven better performance in predicting OC stages and subtypes. However, most of the state-of-the-art deep learning models employ single modality data, which may afford low-level performance due to insufficient representation of important OC characteristics. Furthermore, these deep learning models still lack to the optimization of the model construction, which requires high computational cost to train and deploy them. In this work, a hybrid evolutionary deep learning model, using multi-modal data, is proposed. The established multi-modal fusion framework amalgamates gene modality alongside with histopathological image modality. Based on the different states and forms of each modality, we set up deep feature extraction network, respectively. This includes a predictive antlion-optimized long-short-term-memory model to process gene longitudinal data. Another predictive antlion-optimized convolutional neural network model is included to process histopathology images. The topology of each customized feature network is automatically set by the antlion optimization algorithm to make it realize better performance. After that the output from the two improved networks is fused based upon weighted linear aggregation. The deep fused features are finally used to predict OC stage. A number of assessment indicators was used to compare the proposed model to other nine multi-modal fusion models constructed using distinct evolutionary algorithms. This was conducted using a benchmark for OC and two benchmarks for breast and lung cancers. The results reveal that the proposed model is more precise and accurate in diagnosing OC and the other cancers.

中文翻译:

多模式进化深度学习模型在卵巢癌诊断中的作用

卵巢癌(OC)是女性死亡的常见原因。最近证明,深度学习在预测OC阶段和亚型方面具有更好的性能。但是,大多数最新的深度学习模型都采用单一模态数据,由于重要的OC特性的表示不足,这些数据可能会提供低级性能。此外,这些深度学习模型仍然缺乏模型构建的优化,这需要高昂的计算成本才能训练和部署它们。在这项工作中,提出了一种使用多模式数据的混合进化深度学习模型。建立的多模式融合框架将基因形态与组织病理学图像形态融合在一起。根据每种模态的不同状态和形式,我们分别建立了深度特征提取网络。这包括一个预测性的蚁群优化长短期记忆模型来处理基因纵向数据。包括另一个预测性的蚁群优化卷积神经网络模型,以处理组织病理学图像。每个定制的特征网络的拓扑结构均由蚁群优化算法自动设置,以实现更好的性能。之后,基于加权线性聚合将两个改进网络的输出融合在一起。深度融合特征最终用于预测OC阶段。许多评估指标用于将提出的模型与使用不同进化算法构建的其他九种多模式融合模型进行比较。这是使用OC基准和乳腺癌和肺癌两个基准进行的。
更新日期:2021-04-11
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