Detection of neurofibroma combining radiomics and ensemble learning_Journal of Biomedical Engineering

Authors：

LIU Yunpeng ¹ ,  WENCHENG Dangzhi ² , WANG Ying ³ , WANG Yipeng ¹ , YAN Yuzan ¹ , GAN Kaifeng ⁴ , PAN Tiejun ⁵

1. Information and Computing Science, Ningbo University of Technology, Ningbo, Zhejiang 315000, P. R. China;
2. School of Pharmacy, Qinghai Minzu University, Xining 810000, P. R. China;
3. Institute of Medical Imaging, Henan University, Kaifeng, Henan 475000, P. R. China;
4. Li Huili Hospital Affiliated to Ningbo University, Ningbo, Zhejiang 315000, P. R. China;
5. College of Science & Technology Ningbo University??, Ningbo, Zhejiang 315000, P. R. China;

Corresponding?author：

WENCHENG Dangzhi, Email: 503534641@qq.com

Keywords：

Ensemble learning; Medical image detection; Radiomics; Deep learning; Object detection

DOI：

10.7507/1001-5515.202502037

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

This study proposes an automated neurofibroma detection method for whole-body magnetic resonance imaging (WBMRI) based on radiomics and ensemble learning. A dynamic weighted box fusion mechanism integrating two dimensional (2D) object detection and three dimensional (3D) segmentation is developed, where the fusion weights are dynamically adjusted according to the respective performance of the models in different tasks. The 3D segmentation model leverages spatial structural information to effectively compensate for the limited boundary perception capability of 2D methods. In addition, a radiomics-based false positive reduction strategy is introduced to improve the robustness of the detection system. The proposed method is evaluated on 158 clinical WBMRI cases with a total of 1,380 annotated tumor samples, using five-fold cross-validation. Experimental results show that, compared with the best-performing single model, the proposed approach achieves notable improvements in average precision, sensitivity, and overall performance metrics, while reducing the average number of false positives by 17.68. These findings demonstrate that the proposed method achieves high detection accuracy with enhanced false positive suppression and strong generalization potential.

1.	趙宇督, 彭振偉, 馬駿, 等. 基于多尺度注意力機制的三維卷積神經網絡肺結節檢測算法. 生物醫學工程學雜志, 2022, 39(2): 320-328.
2.	Doshi R, Hiran K K, Doppala B P, et al. Deep belief network-based image processing for local directional segmentation in brain tumor detection. Journal of Electronic Imaging, 2023, 32(6): 062502.
3.	Wu S, Hong G, Xu A, et al. Artificial intelligence-based model for lymph node metastases detection on whole slide images in bladder cancer: a retrospective, multicentre, diagnostic study. Lancet Oncol, 2023, 24(4): 360-370.
4.	史彩娟, 鄭遠帆, 任弼娟, 等. 單域泛化X-ray乳腺腫瘤檢測. 中國圖象圖形學報, 2024, 29(3): 725-740.
5.	周濤, 葉鑫宇, 趙雅楠, 等. 跨模態注意力YOLOv5的PET/CT肺部腫瘤檢測. 中國圖象圖形學報, 2024, 29(4): 1070-1084.
6.	Sunnetci K M, Kaba E, Celiker F B, et al. Deep network-based comprehensive parotid gland tumor detection. Acad Radiol, 2024, 31(1): 157-167.
7.	Huang A, Guo D Z, Zhang X, et al. Serial circulating tumor DNA profiling predicts tumor recurrence after liver transplantation for liver cancer. Hepatol Int, 2024, 18(1): 254-264.
8.	Manjunath R V, Ghanshala A, Kwadiki K. Deep learning algorithm performance evaluation in detection and classification of liver disease using CT images. Multimedia Tools and Applications, 2024, 83: 2773-2790.
9.	Gao C, Wu L, Wu W, et al. Deep learning in pulmonary nodule detection and segmentation: a systematic review. European Radiology, 2025, 35(1): 255-266.
10.	周濤, 趙雅楠, 陸惠玲, 等. 醫學圖像實例分割: 從有候選區域向無候選區域. 生物醫學工程學雜志, 2022, 39(6): 1218-1232.
11.	Liu F, Chen Z, Sun P. Detection and segmentation of pulmonary nodules based on improved 3D VNet algorithm//International Conference on Algorithms, Microchips and Network Applications (AMNA), Zhuhai: SPIE, 2022: 51-59.
12.	Naseer I, Akram S, Masood T, et al. Lung cancer classification using modified U-Net based lobe segmentation and nodule detection. IEEE Access, 2023, 11: 60279-60291.
13.	汪華登, 王雪馨, 黎兵兵, 等. GZMH: 用于有絲分裂細胞核檢測和分割的乳腺癌病理圖像數據集. 中國圖象圖形學報, 2024, 29(3): 608-619.
14.	Do?an K, Sel?uk T, Alkan A. An enhanced mask R-CNN approach for pulmonary embolism detection and segmentation. Diagnostics, 2024, 14(11): 1102-1113.
15.	Sirazitdinov I, Kholiavchenko M, Mustafaev T, et al. Deep neural network ensemble for pneumonia localization from a large-scale chest X-ray database. Computers & Electrical Engineering, 2019, 78: 388-399.
16.	Sreelakshmi S, Malu G, Sherly E, et al. M-Net: an encoder-decoder architecture for medical image analysis using ensemble learning. Results in Engineering, 2023, 17: 100927.
17.	Patil S, Kirange D. Ensemble of deep learning models for brain tumor detection. Procedia Computer Science, 2023, 218: 2468-2479.
18.	Munshi R M, Cascone L, Alturki N, et al. A novel approach for breast cancer detection using optimized ensemble learning framework and XAI. Image and Vision Computing, 2024, 142: 104910.
19.	Albuquerque C, Henriques R, Castelli M. Deep learning-based object detection algorithms in medical imaging: systematic review. Heliyon, 2025, 11(1): e41137.
20.	魏然, 林侃如, 郭翌, 等. 基于影像組學預測胰腺囊性腫瘤Ki67分子標記物的可行性研究. 生物醫學工程學雜志, 2019, 36(1): 1-6.
21.	李影, 邱麗華. 宮頸癌MRI影像組學研究現狀及進展. 中國醫學影像技術, 2023, 39(9): 1407-1410.
22.	Tortora M, Gemini L, Scaravilli A, et al. Radiomics applications in head and neck tumor imaging: a narrative review. Cancers, 2023, 15(4): 1174.
23.	祁納, 趙軍. 基于MRI影像組學和機器學習診斷抑郁癥研究進展. 中國醫學影像技術, 2024, 40(3): 455-458.
24.	魏江龍, 喬英. CT影像組學在腎細胞癌診斷與個性化治療中的研究進展. 分子影像學雜志, 2025, 48(8): 1050-1056.
25.	吳樹劍, 俞詠梅, 范莉芳,等. 基于增強CT深度學習影像組學術前預測胸腺瘤風險分類. 中國腫瘤臨床, 2023, 50(19): 999-1005.
26.	Yang D, Ren G, Ni R, et al. Deep learning attention-guided radiomics for COVID-19 chest radiograph classification. Quantitative Imaging in Medicine and Surgery, 2023, 13(2): 572-584.
27.	Xia T, Zhao B, Li B, et al. MRI-based radiomics and deep learning in biological characteristics and prognosis of hepatocellular carcinoma: opportunities and challenges. Journal of Magnetic Resonance Imaging, 2024, 59(3): 767-783.
28.	李宇璞, 趙鵬飛, 張小娟, 等. MRI影像組學聯合ResNet101深度學習鑒別腰椎布魯氏菌性脊柱炎與脊柱轉移癌. 中國醫學影像技術, 2025, 41(6): 958-962.
29.	Cai Z, Vasconcelos N. Cascade R-CNN: high quality object detection and instance segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(5): 1483-1498.
30.	Tan M, Pang R, Le Q V. Efficientdet: scalable and efficient object detection//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle: IEEE, 2020: 10781-10790.
31.	Wang S, Zhu Y, Lee S, et al. Global-local attention network with multi-task uncertainty loss for abnormal lymph node detection in MR images. Medical Image Analysis, 2022, 77: 102345.
32.	Li F, Zhang H, Xu H, et al. Mask dino: towards a unified transformer-based framework for object detection and segmentation//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver: IEEE, 2023: 3041-3050.
33.	Jiang Y, Chang S, Wang Z. TransGAN: two pure transformers can make one strong GAN, and that can scale up. Advances in Neural Information Processing Systems, 2021, 34: 14745-14758.
34.	Tran M, Vo-Ho V K, Le N T. 3DConvCaps: 3DUnet with convolutional capsule encoder for medical image segmentation//International Conference on Pattern Recognition (ICPR), Bangalore: IEEE, 2022: 4392-4398.
35.	Hatamizadeh A, Tang Y, Nath V, et al. UNETR: transformers for 3D medical image segmentation//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), New York: IEEE, 2022: 574-584.
36.	Liu Y, Zhang J, She Z, et al. SAMM (segment any medical model): a 3D slicer integration to sam. arXiv preprint, 2023, arXiv: 2304.05622.

1. 趙宇督, 彭振偉, 馬駿, 等. 基于多尺度注意力機制的三維卷積神經網絡肺結節檢測算法. 生物醫學工程學雜志, 2022, 39(2): 320-328.
2. Doshi R, Hiran K K, Doppala B P, et al. Deep belief network-based image processing for local directional segmentation in brain tumor detection. Journal of Electronic Imaging, 2023, 32(6): 062502.
3. Wu S, Hong G, Xu A, et al. Artificial intelligence-based model for lymph node metastases detection on whole slide images in bladder cancer: a retrospective, multicentre, diagnostic study. Lancet Oncol, 2023, 24(4): 360-370.
4. 史彩娟, 鄭遠帆, 任弼娟, 等. 單域泛化X-ray乳腺腫瘤檢測. 中國圖象圖形學報, 2024, 29(3): 725-740.
5. 周濤, 葉鑫宇, 趙雅楠, 等. 跨模態注意力YOLOv5的PET/CT肺部腫瘤檢測. 中國圖象圖形學報, 2024, 29(4): 1070-1084.
6. Sunnetci K M, Kaba E, Celiker F B, et al. Deep network-based comprehensive parotid gland tumor detection. Acad Radiol, 2024, 31(1): 157-167.
7. Huang A, Guo D Z, Zhang X, et al. Serial circulating tumor DNA profiling predicts tumor recurrence after liver transplantation for liver cancer. Hepatol Int, 2024, 18(1): 254-264.
8. Manjunath R V, Ghanshala A, Kwadiki K. Deep learning algorithm performance evaluation in detection and classification of liver disease using CT images. Multimedia Tools and Applications, 2024, 83: 2773-2790.
9. Gao C, Wu L, Wu W, et al. Deep learning in pulmonary nodule detection and segmentation: a systematic review. European Radiology, 2025, 35(1): 255-266.
10. 周濤, 趙雅楠, 陸惠玲, 等. 醫學圖像實例分割: 從有候選區域向無候選區域. 生物醫學工程學雜志, 2022, 39(6): 1218-1232.
11. Liu F, Chen Z, Sun P. Detection and segmentation of pulmonary nodules based on improved 3D VNet algorithm//International Conference on Algorithms, Microchips and Network Applications (AMNA), Zhuhai: SPIE, 2022: 51-59.
12. Naseer I, Akram S, Masood T, et al. Lung cancer classification using modified U-Net based lobe segmentation and nodule detection. IEEE Access, 2023, 11: 60279-60291.
13. 汪華登, 王雪馨, 黎兵兵, 等. GZMH: 用于有絲分裂細胞核檢測和分割的乳腺癌病理圖像數據集. 中國圖象圖形學報, 2024, 29(3): 608-619.
14. Do?an K, Sel?uk T, Alkan A. An enhanced mask R-CNN approach for pulmonary embolism detection and segmentation. Diagnostics, 2024, 14(11): 1102-1113.
15. Sirazitdinov I, Kholiavchenko M, Mustafaev T, et al. Deep neural network ensemble for pneumonia localization from a large-scale chest X-ray database. Computers & Electrical Engineering, 2019, 78: 388-399.
16. Sreelakshmi S, Malu G, Sherly E, et al. M-Net: an encoder-decoder architecture for medical image analysis using ensemble learning. Results in Engineering, 2023, 17: 100927.
17. Patil S, Kirange D. Ensemble of deep learning models for brain tumor detection. Procedia Computer Science, 2023, 218: 2468-2479.
18. Munshi R M, Cascone L, Alturki N, et al. A novel approach for breast cancer detection using optimized ensemble learning framework and XAI. Image and Vision Computing, 2024, 142: 104910.
19. Albuquerque C, Henriques R, Castelli M. Deep learning-based object detection algorithms in medical imaging: systematic review. Heliyon, 2025, 11(1): e41137.
20. 魏然, 林侃如, 郭翌, 等. 基于影像組學預測胰腺囊性腫瘤Ki67分子標記物的可行性研究. 生物醫學工程學雜志, 2019, 36(1): 1-6.
21. 李影, 邱麗華. 宮頸癌MRI影像組學研究現狀及進展. 中國醫學影像技術, 2023, 39(9): 1407-1410.
22. Tortora M, Gemini L, Scaravilli A, et al. Radiomics applications in head and neck tumor imaging: a narrative review. Cancers, 2023, 15(4): 1174.
23. 祁納, 趙軍. 基于MRI影像組學和機器學習診斷抑郁癥研究進展. 中國醫學影像技術, 2024, 40(3): 455-458.
24. 魏江龍, 喬英. CT影像組學在腎細胞癌診斷與個性化治療中的研究進展. 分子影像學雜志, 2025, 48(8): 1050-1056.
25. 吳樹劍, 俞詠梅, 范莉芳,等. 基于增強CT深度學習影像組學術前預測胸腺瘤風險分類. 中國腫瘤臨床, 2023, 50(19): 999-1005.
26. Yang D, Ren G, Ni R, et al. Deep learning attention-guided radiomics for COVID-19 chest radiograph classification. Quantitative Imaging in Medicine and Surgery, 2023, 13(2): 572-584.
27. Xia T, Zhao B, Li B, et al. MRI-based radiomics and deep learning in biological characteristics and prognosis of hepatocellular carcinoma: opportunities and challenges. Journal of Magnetic Resonance Imaging, 2024, 59(3): 767-783.
28. 李宇璞, 趙鵬飛, 張小娟, 等. MRI影像組學聯合ResNet101深度學習鑒別腰椎布魯氏菌性脊柱炎與脊柱轉移癌. 中國醫學影像技術, 2025, 41(6): 958-962.
29. Cai Z, Vasconcelos N. Cascade R-CNN: high quality object detection and instance segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(5): 1483-1498.
30. Tan M, Pang R, Le Q V. Efficientdet: scalable and efficient object detection//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle: IEEE, 2020: 10781-10790.
31. Wang S, Zhu Y, Lee S, et al. Global-local attention network with multi-task uncertainty loss for abnormal lymph node detection in MR images. Medical Image Analysis, 2022, 77: 102345.
32. Li F, Zhang H, Xu H, et al. Mask dino: towards a unified transformer-based framework for object detection and segmentation//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver: IEEE, 2023: 3041-3050.
33. Jiang Y, Chang S, Wang Z. TransGAN: two pure transformers can make one strong GAN, and that can scale up. Advances in Neural Information Processing Systems, 2021, 34: 14745-14758.
34. Tran M, Vo-Ho V K, Le N T. 3DConvCaps: 3DUnet with convolutional capsule encoder for medical image segmentation//International Conference on Pattern Recognition (ICPR), Bangalore: IEEE, 2022: 4392-4398.
35. Hatamizadeh A, Tang Y, Nath V, et al. UNETR: transformers for 3D medical image segmentation//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), New York: IEEE, 2022: 574-584.
36. Liu Y, Zhang J, She Z, et al. SAMM (segment any medical model): a 3D slicer integration to sam. arXiv preprint, 2023, arXiv: 2304.05622.

Journal of Biomedical Engineering

Latest ArticlesDetection of neurofibroma combining radiomics and ensemble learning

Abstract Full text Figures/Tables Video References Cited by

Format

Content