Background and Objective

Bone tumors present significant challenges in orthopedic medicine due to variations in clinical treatment approaches for different tumor types, which includes benign, malignant, and intermediate cases. Convolutional Neural Networks (CNNs) have emerged as prominent models for tumor classification. However, their limited perception ability hinders the acquisition of global structural information, potentially affecting classification accuracy. To address this limitation, we propose an optimized deep learning algorithm for precise classification of diverse bone tumors.

Materials and Methods

Our dataset comprises 786 computed tomography (CT) images of bone tumors, featuring sections from two distinct bone species, namely the tibia and femur. Sourced from The Second Affiliated Hospital of Fujian Medical University, the dataset was meticulously preprocessed with noise reduction techniques. We introduce a novel fusion model, VGG16-ViT, leveraging the advantages of the VGG-16 network and the Vision Transformer (ViT) model. Specifically, we select 27 features from the third layer of VGG-16 and input them into the Vision Transformer encoder for comprehensive training. Furthermore, we evaluate the impact of secondary migration using CT images from Xiangya Hospital for validation.

Results

The proposed fusion model demonstrates notable improvements in classification performance. It effectively reduces the training time while achieving an impressive classification accuracy rate of 97.6%, marking a significant enhancement of 8% in sensitivity and specificity optimization. Furthermore, the investigation into secondary migration's effects on experimental outcomes across the three models reveals its potential to enhance system performance.

Conclusion

Our novel VGG-16 and Vision Transformer joint network exhibits robust classification performance on bone tumor datasets. The integration of these models enables precise and efficient classification, accommodating the diverse characteristics of different bone tumor types. This advancement holds great significance for the early detection and prognosis of bone tumor patients in the future.

中文翻译：

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VGG-16 和 ViT 模型的融合可改善计算机断层扫描中的骨肿瘤分类

背景和目的

由于不同肿瘤类型（包括良性、恶性和中间型病例）的临床治疗方法存在差异，骨肿瘤对骨科医学提出了重大挑战。卷积神经网络（CNN）已成为肿瘤分类的重要模型。然而，它们有限的感知能力阻碍了全局结构信息的获取，可能影响分类准确性。为了解决这一限制，我们提出了一种优化的深度学习算法，用于对不同骨肿瘤进行精确分类。

材料和方法

我们的数据集包含 786 张骨肿瘤的计算机断层扫描 (CT) 图像，其中包含来自两种不同骨种（即胫骨和股骨）的切片。数据集来源于福建医科大学第二附属医院，采用降噪技术精心预处理。我们引入了一种新颖的融合模型 VGG16-ViT，利用 VGG-16 网络和 Vision Transformer (ViT) 模型的优势。具体来说，我们从VGG-16的第三层中选择27个特征，并将其输入到Vision Transformer编码器中进行综合训练。此外，我们使用湘雅医院的 CT 图像评估二次迁移的影响进行验证。

结果

所提出的融合模型展示了分类性能的显着改进。它有效减少了训练时间，同时实现了高达 97.6% 的令人印象深刻的分类准确率，灵敏度和特异性优化显着提升了 8%。此外，对二次迁移对三个模型实验结果的影响的研究揭示了其增强系统性能的潜力。

结论

我们新颖的 VGG-16 和 Vision Transformer 联合网络在骨肿瘤数据集上表现出强大的分类性能。这些模型的集成可以实现精确有效的分类，适应不同骨肿瘤类型的不同特征。这一进展对于未来骨肿瘤患者的早期发现和预后具有重要意义。