Thyroid cancer pathological classification in ultrasound images based on faster R-CNN network

Shiyang Zheng; Zenan Guo; Chen Chen; Bojian Feng; Feijian Lai

doi:10.1117/12.3005926

10 October 2023 Thyroid cancer pathological classification in ultrasound images based on faster R-CNN network

Shiyang Zheng, Zenan Guo, Chen Chen, Bojian Feng, Feijian Lai

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 127995K (2023) https://doi.org/10.1117/12.3005926
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

Objective: This study explored the performance of deep learning Faster R-CNN network for the automatic detection of nodules in thyroid ultrasound images and its efficacy in predicting different pathological classifications of thyroid malignant nodules. Methods: We retrospectively collected 1762 thyroid cancer ultrasound images from 548 patients, of which 80% of the enhanced ultrasound images were used for training and testing, and the remaining 20% were used for validation. The pathological classification of all nodules was confirmed by pathology. We also evaluated the effectiveness of Faster RCNN models based on different backbone networks in predicting the pathological classification of thyroid cancer. Results: The Faster R-CNN model using ResNet50 as the backbone network in this study had an AUROC of 0.873 and an mAP of 84.04% for pathological classification of malignant thyroid nodules. The accuracy rate for Papillary Thyroid Carcinoma (PTC) was 84.89%, for Medullary Thyroid Carcinoma (MTC) was 89.41%, for Anaplastic Thyroid Carcinoma (ATC) was 82.38%, and for Follicular Thyroid Carcinoma (FTC) was 81.36%. The overall accuracy rate of the final classification was 84.59%. The recall rates of the best model were 87.37%, 85.80%, 83.48%, and 82.30% for PTC, MTC, ATC, and FTC, respectively. Conclusion: This study demonstrates that deep learning Faster R-CNN network can detect thyroid nodules and has good diagnostic efficacy in distinguishing pathological classifications of thyroid malignant nodules. It has great potential for clinical applications.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shiyang Zheng, Zenan Guo, Chen Chen, Bojian Feng, and Feijian Lai "Thyroid cancer pathological classification in ultrasound images based on faster R-CNN network", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 127995K (10 October 2023); https://doi.org/10.1117/12.3005926

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KEYWORDS

Thyroid

Ultrasonography

Cancer

Tumor growth modeling

Image classification

Data modeling

Diagnostics

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