Research on medical image discrimination method based on improved 3D-CNN model

In response to the current contradiction of rapid growth in lung CT image data and insufficient human diagnostic capacity, we propose a medical image discrimination method based on an improved 3D-CNN model. This method first segments the problematic regions in the images. Then, it utilizes a category-weighted improved loss function and increases the convolutional layers to construct a 3D-CNN model. Finally, the model is fine-tuned to predict the probability of malignancy for tumors. Additionally, we leverage repeated sampling and data augmentation to address data imbalance issues, thereby enhancing the effectiveness of the model. The experimental results indicate that this discrimination method, compared to the original 3D-CNN model, has shown a significant improvement. The F1-score has increased by 27.3%, Precision by 34.5%, and Recall by 13.8%. It can more effectively detect pulmonary nodules, thereby reducing the areas that doctors need to assess and enhancing overall work efficiency.