基于多模态图像融合的深度集成方法用于肺癌高效分类
Deep Ensembling with Multimodal Image Fusion for Efficient Classification of Lung Cancer
AI总结：
本研究提出一种基于PCA和自编码器的多模态图像融合方法，并开发深度集成多模态融合（DEMF）分类器，结合Grad-CAM可视化与数据增强策略，在三个公开数据集上有效缓解数据稀缺问题并实现肺癌高效分类。
摘要：
本研究聚焦于从多模态肺部图像中对癌变和健康切片进行分类。研究中使用的数据包括计算机断层扫描（CT）和正电子发射断层扫描（PET）图像。所提出的策略通过利用主成分分析（PCA）和自编码器实现PET和CT图像的融合。随后，开发了一种新的基于集成的分类器——深度集成多模态融合（DEMF），采用多数投票法对受检样本图像进行分类。采用梯度加权类激活映射（Grad-CAM）来可视化受癌症影响图像的分类准确性。鉴于样本量有限，在训练阶段采用了随机图像增强策略。DEMF网络有助于缓解计算机辅助医学图像分析中数据稀缺的挑战。所提出的网络在三个公开可用的数据集上与最先进的网络进行了比较。该网络在准确率、F1分数、精确率和召回率等指标上优于其他网络。研究结果突出了所提出网络的有效性。
英文摘要：
This study focuses on the classification of cancerous and healthy slices from multimodal lung images. The data used in the research comprises Computed Tomography (CT) and Positron Emission Tomography (PET) images. The proposed strategy achieves the fusion of PET and CT images by utilizing Principal Component Analysis (PCA) and an Autoencoder. Subsequently, a new ensemble-based classifier developed, Deep Ensembled Multimodal Fusion (DEMF), employing majority voting to classify the sample images under examination. Gradient-weighted Class Activation Mapping (Grad-CAM) employed to visualize the classification accuracy of cancer-affected images. Given the limited sample size, a random image augmentation strategy employed during the training phase. The DEMF network helps mitigate the challenges of scarce data in computer-aided medical image analysis. The proposed network compared with state-of-the-art networks across three publicly available datasets. The network outperforms others based on the metrics - Accuracy, F1-Score, Precision, and Recall. The investigation results highlight the effectiveness of the proposed network.
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