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

TL;DR

This paper introduces a deep learning framework using a pre-trained MobileNetV2 model for classifying lung tumor CT images, achieving high accuracy and demonstrating significant potential in medical diagnosis and healthcare market applications.

Contribution

It develops a transfer learning-based deep neural network with improved feature extraction for lung tumor classification, enhancing diagnostic accuracy over traditional methods.

Findings

Achieved 99.6% accuracy on test data

Significantly improved feature extraction capabilities

Demonstrated potential for AI in medical diagnosis

Abstract

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.