In the medical field, accurate diagnosis of lung cancer is crucial for treatment. Traditional manual analysis methods have significant limitations in terms of accuracy and efficiency. To address this issue, this paper proposes a deep learning network framework based on the pre-trained MobileNetV2 model, initialized with weights from the ImageNet-1K dataset (version 2). The last layer of the model (the fully connected layer) is replaced with a new fully connected layer, and a softmax activation function is added to efficiently classify three types of lung cancer CT scan images. Experimental results show that the model achieves an accuracy of 99.6% on the test set, with significant improvements in feature extraction compared to traditional models.With the rapid development of artificial intelligence technologies, deep learning applications in medical image processing are bringing revolutionary changes to the healthcare industry. AI-based lung cancer detection systems can significantly improve diagnostic efficiency, reduce the workload of doctors, and occupy an important position in the global healthcare market. The potential of AI to improve diagnostic accuracy, reduce medical costs, and promote precision medicine will have a profound impact on the future development of the healthcare industry.

本研究针对传统手动分析肺癌CT图像的准确性和效率不足的问题，提出了一种基于预训练MobileNetV2模型的深度学习网络框架。该模型的实验结果显示其在测试集上达到了99.6%的准确率，表明其在特征提取方面显著优于传统模型，有助于提高肺癌的诊断效率和降低医疗成本。

A CT Image Classification Network Framework for Lung Tumors Based on
  Pre-trained MobileNetV2 Model and Transfer learning, And Its Application and
  Market Analysis in the Medical field

基于预训练MobileNetV2模型和迁移学习的肺肿瘤CT图像分类网络框架及其在医疗领域的应用与市场分析