College of Software, Xinjiang University, Urumqi 830046, China.
College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China; Xinjiang Cloud Computing Application Laboratory, Karamay 834099, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2023 Dec 15;303:123147. doi: 10.1016/j.saa.2023.123147. Epub 2023 Jul 13.
Cervical cancer ranks among the most prevalent forms of gynecological malignancies. Timely identification of cervical lesions and prompt intervention can effectively prevent the development of cervical cancer or enhance patients' chances of survival. In this study, we propose an innovative method based on Raman spectroscopy, i.e., a multi-level SENet attention mechanism feature fusion architecture (MAFA) for rapid diagnosis of cervical cancer and precancerous lesions. The convolution process of this architecture can extract features from shallow to deep layers, and the attention mechanism is added to achieve the fusion of features from different layers. The added attention mechanism can automatically determine the importance of each layer feature channel and assign weight values to that layer according to the importance of each layer to achieve the purpose of focusing the model on certain waveform features and improve the targeting of model learning. We collected Raman spectra of 212 cervical tissues containing cervical cancer and its precancerous lesions.The experimental results show that MAFA can effectively improve the diagnostic accuracy of VGGNet, GoogLeNet and ResNet models in the validation of Raman spectral data of cervical tissue. Among them, ResNet performed the best, with the highest average accuracy, precision, recall and F1-Score of 82.36%, 84.00%, 82.35% and 82.26%, respectively, when no feature fusion was performed. The evaluation metrics improved by 4.91%, 3.97%, 4.97%, and 5.06%, respectively, after using the MAFA; they also improved by 4.16%, 2.90%, 4.17%, and 4.32%, respectively, compared with the model that directly performs feature fusion without using the attention mechanism. Therefore, the MAFA proposed in this study is better than that of the neural network that directly fuses the features of each convolutional layer. The experimental results show that the performance of the MAFA proposed in this paper is significantly higher than that of traditional deep learning algorithms, indicating that the present architecture can effectively improve the diagnostic accuracy of deep learning networks for cervical cancer.
宫颈癌是最常见的妇科恶性肿瘤之一。及时识别宫颈病变并及时干预，可以有效预防宫颈癌的发生，提高患者的生存率。在这项研究中，我们提出了一种基于拉曼光谱的创新方法，即基于多级 SENet 注意力机制特征融合架构（MAFA）的快速宫颈癌和癌前病变诊断方法。该架构的卷积过程可以从浅层到深层提取特征，并且添加注意力机制可以实现来自不同层的特征融合。添加的注意力机制可以自动确定每个层特征通道的重要性，并根据每个层的重要性为该层分配权重值，从而达到模型聚焦于某些波形特征的目的，并提高模型学习的针对性。我们收集了包含宫颈癌及其癌前病变的 212 例宫颈组织的拉曼光谱。实验结果表明，MAFA 可以有效提高 VGGNet、GoogLeNet 和 ResNet 模型在验证宫颈组织拉曼光谱数据时的诊断准确率。其中，ResNet 在没有进行特征融合时表现最好，平均准确率、精度、召回率和 F1-Score 分别为 82.36%、84.00%、82.35%和 82.26%。使用 MAFA 后，评估指标分别提高了 4.91%、3.97%、4.97%和 5.06%；与不使用注意力机制直接进行特征融合的模型相比，分别提高了 4.16%、2.90%、4.17%和 4.32%。因此，本研究提出的 MAFA 优于直接融合各卷积层特征的神经网络。实验结果表明，本文提出的 MAFA 性能明显高于传统深度学习算法，表明该架构可以有效提高深度学习网络对宫颈癌的诊断准确率。
