融合NASNet与ViT的模型可精准识别五类肺病，准确率达98.9%。
An Integrated Deep Learning Framework Leveraging NASNet and Vision Transformer with MixProcessing for Accurate and Precise Diagnosis of Lung Diseases
- 用NASNet与ViT结合，兼顾局部特征与全局注意力。
- 采用混合预处理提升性能，准确率98.9%，F1-score达0.989。
- 模型仅25.6MB，推理仅需12.4秒，适合临床实时应用。
肺是呼吸关键器官，其疾病如肺炎、结核、新冠和肺癌严重威胁健康，亟需早期精准诊断。本文提出一种新型深度学习框架NASNet-ViT，融合NASNet的卷积能力与Vision Transformer（ViT）的全局注意力机制，将肺部状况分为五类：肺癌、新冠、肺炎、结核和正常。采用名为MixProcessing的多维度预处理策略，整合小波变换、自适应直方图均衡化与形态学滤波。该模型达到98.9%准确率、0.99敏感性、0.989 F1-score与0.987特异性，优于MixNet-LD、D-ResNet、MobileNet及ResNet50等先进架构。模型体积仅为25.6MB，计算耗时仅12.4秒，适用于实时临床环境。结果表明NASNet-ViT在特征提取与疾病识别方面具有极高精度，为肺病医学影像分析提供了可靠、可扩展的解决方案。
原文摘要 · Abstract (English)
The lungs are the essential organs of respiration, and this system is significant in the carbon dioxide and exchange between oxygen that occurs in human life. However, several lung diseases, which include pneumonia, tuberculosis, COVID-19, and lung cancer, are serious healthiness challenges and demand early and precise diagnostics. The methodological study has proposed a new deep learning framework called NASNet-ViT, which effectively incorporates the convolution capability of NASNet with the global attention mechanism capability of Vision Transformer ViT. The proposed model will classify the lung conditions into five classes: Lung cancer, COVID-19, pneumonia, TB, and normal. A sophisticated multi-faceted preprocessing strategy called MixProcessing has been used to improve diagnostic accuracy. This preprocessing combines wavelet transform, adaptive histogram equalization, and morphological filtering techniques. The NASNet-ViT model performs at state of the art, achieving an accuracy of 98.9%, sensitivity of 0.99, an F1-score of 0.989, and specificity of 0.987, outperforming other state of the art architectures such as MixNet-LD, D-ResNet, MobileNet, and ResNet50. The model's efficiency is further emphasized by its compact size, 25.6 MB, and a low computational time of 12.4 seconds, hence suitable for real-time, clinically constrained environments. These results reflect the high-quality capability of NASNet-ViT in extracting meaningful features and recognizing various types of lung diseases with very high accuracy. This work contributes to medical image analysis by providing a robust and scalable solution for diagnostics in lung diseases.
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