用于检测医学图像深度伪造的深度卷积神经网络比较分析
Comparative Analysis of Deep Convolutional Neural Networks for Detecting Medical Image Deepfakes
AI总结：
本文综合评估13种DCNN在医学图像深度伪造检测中的表现，发现DenseNet169综合指标最佳，ResNet50V2精确率与特异度最优，MobileNetV3Large速度最快且参数较少。
摘要：
生成对抗网络（GANs）已在包括医学成像在内的多种应用中展现出显著进展。尽管众多最先进的深度卷积神经网络（DCNN）架构以其熟练的特征提取能力而闻名，本文研究了它们在医学图像深度伪造检测情境中的有效性。主要目标是通过对13种最先进的DCNN进行综合评估，有效区分真实医学图像与被篡改或操纵的医学图像。研究通过多种评估指标衡量性能，涵盖时间效率和计算资源需求方面的考量。研究结果表明，ResNet50V2在精确率和特异度方面表现优异，而DenseNet169则在准确率、召回率和F1分数方面表现突出。本文研究了一个模型比另一个模型更受青睐的具体场景。此外，MobileNetV3Large提供了具有竞争力的性能，在所考察的DCNN模型中速度最快，同时保持相对较少的参数量。本文还评估了所考察DCNN的潜在空间可分离性质量，结果显示DenseNet和EfficientNet模型族在这方面均具有优势，意味着它们对医学图像深度伪造具有更高水平的理解。本研究中的实验分析为医学成像领域的深度伪造图像检测领域贡献了有价值的见解。
英文摘要：
Generative Adversarial Networks (GANs) have exhibited noteworthy advancements across various applications, including medical imaging. While numerous state-of-the-art Deep Convolutional Neural Network (DCNN) architectures are renowned for their proficient feature extraction, this paper investigates their efficacy in the context of medical image deepfake detection. The primary objective is to effectively distinguish real from tampered or manipulated medical images by employing a comprehensive evaluation of 13 state-of-the-art DCNNs. Performance is assessed across diverse evaluation metrics, encompassing considerations of time efficiency and computational resource requirements. Our findings reveal that ResNet50V2 excels in precision and specificity, whereas DenseNet169 is distinguished by its accuracy, recall, and F1-score. We investigate the specific scenarios in which one model would be more favorable than another. Additionally, MobileNetV3Large offers competitive performance, emerging as the swiftest among the considered DCNN models while maintaining a relatively small parameter count. We also assess the latent space separability quality across the examined DCNNs, showing superiority in both the DenseNet and EfficientNet model families and entailing a higher understanding of medical image deepfakes. The experimental analysis in this research contributes valuable insights to the field of deepfake image detection in the medical imaging domain.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
