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

Xue Tang

doi:10.1117/12.3029848

21 June 2024 Research on medical image discrimination method based on improved 3D-CNN model

Xue Tang

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131672S (2024) https://doi.org/10.1117/12.3029848
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xue Tang "Research on medical image discrimination method based on improved 3D-CNN model", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131672S (21 June 2024); https://doi.org/10.1117/12.3029848

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