标题： 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 显示英文标题

标题： 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

摘要： 在医学领域，对肺癌的准确诊断对于治疗至关重要。 传统的手动分析方法在准确性和效率方面存在显著局限性。 为了解决这个问题，本文提出了一种基于预训练的MobileNetV2模型的深度学习网络框架，该模型使用来自ImageNet-1K数据集（版本2）的权重进行初始化。 模型的最后一层（全连接层）被替换为一个新的全连接层，并添加了softmax激活函数，以高效地分类三种类型的肺癌CT扫描图像。 实验结果表明，该模型在测试集上的准确率达到99.6%，与传统模型相比，在特征提取方面有显著提升。随着人工智能技术的快速发展，深度学习在医学图像处理中的应用正在给医疗行业带来革命性的变化。 基于人工智能的肺癌检测系统可以显著提高诊断效率，减轻医生的工作负担，并在全球医疗市场中占据重要地位。 人工智能在提高诊断准确性、降低医疗成本和推动精准医疗方面的潜力，将对医疗行业的未来发展产生深远影响。 显示英文摘要

摘要： 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.