Ultrasound Image Lesion Detection Algorithm Optimized by Feature Feedback Mechanism

DING Jianrui; WANG Lingtao; TANG Fenghe; NING Chunping

doi:10.11999/JEIT230385

Volume 46 Issue 3

Mar. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(3): 1013-1021

DING Jianrui, WANG Lingtao, TANG Fenghe, NING Chunping. Ultrasound Image Lesion Detection Algorithm Optimized by Feature Feedback Mechanism[J]. Journal of Electronics & Information Technology, 2024, 46(3): 1013-1021. doi: 10.11999/JEIT230385

Citation:

DING Jianrui, WANG Lingtao, TANG Fenghe, NING Chunping. Ultrasound Image Lesion Detection Algorithm Optimized by Feature Feedback Mechanism[J]. Journal of Electronics & Information Technology, 2024, 46(3): 1013-1021. doi: 10.11999/JEIT230385

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Ultrasound Image Lesion Detection Algorithm Optimized by Feature Feedback Mechanism

doi: 10.11999/JEIT230385 cstr: 32379.14.JEIT230385

1.
School of Computer Science and Technology, Harbin Institute of Technology (Weihai), Weihai 264209, China
2.
Ultrasound Department, The Affiliated Hospital of Qingdao University, Qingdao 266003, China

Funds: The National Natural Science Foundation of China (U22A2033), The Natural Science Foundation of Shandong Province (ZR2020MH290)

Received Date: 2023-05-08
Accepted Date: 2023-08-21
Rev Recd Date: 2023-08-18

Available Online: 2023-08-24

Publish Date: 2024-03-27

Abstract

Abstract

A lesion detection method in ultrasound images based on feature feedback mechanism is proposed to realize real-time accurate localization and detection of ultrasound lesions. The proposed method consists of two parts: feature extraction network based on feature feedback mechanism and adaptive detection head based on divide-and-conquer strategy. The feature feedback network fully learns the global context information and local low-level semantic details of ultrasound images through feedback feature selection and weighted fusion calculation to improve the recognition ability of local lesion features. The adaptive detection head performs divide-and-conquer preprocessing on the multi-level features extracted by the feature feedback network. By combining physiological prior knowledge and feature convolution, adaptive modeling of lesion shape and scale features is performed on features at all levels to enhance the detection effect of the detection head on lesions of different sizes under multi-level features. The proposed method is tested on the thyroid ultrasound image dataset, and 70.3% AP, 99.0% AP50 and 88.4% AP75 are obtained. Experimental results show that the proposed algorithm can achieve more accurate real-time detection and positioning of ultrasound image lesions in comparison with mainstream detection algorithm.
- Lesion detection,
- Feature feedback,
- Adaptive detection head

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References(27)

References

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