التفاصيل البيبلوغرافية
| العنوان: |
3D residual attention hierarchical fusion for real-time detection of the prostate capsule |
| المؤلفون: |
Shixiao Wu, Chengcheng Guo, Ayixiamu Litifu, Zhiwei Wang |
| المصدر: |
BMC Medical Imaging, Vol 24, Iss 1, Pp 1-12 (2024) |
| بيانات النشر: |
BMC, 2024. |
| سنة النشر: |
2024 |
| المجموعة: |
LCC:Medical technology |
| مصطلحات موضوعية: |
SimAM, Residual attention hierarchical fusion, Deep learning, Machine learning, Prostate capsule, Medical technology, R855-855.5 |
| الوصف: |
Abstract Background For prostate electrosurgery, where real-time surveillance screens are relied upon for operations, manual identification of the prostate capsule remains the primary method. With the need for rapid and accurate detection becoming increasingly urgent, we set out to develop a deep learning approach for detecting the prostate capsule using endoscopic optical images. Methods Our method involves utilizing the Simple, Parameter-Free Attention Module(SimAM) residual attention fusion module to enhance the extraction of texture and detail information, enabling better feature extraction capabilities. This enhanced detail information is then hierarchically transferred from lower to higher levels to aid in the extraction of semantic information. By employing a forward feature-by-feature hierarchical fusion network based on the 3D residual attention mechanism, we have proposed an improved single-shot multibox detector model. Results Our proposed model achieves a detection precision of 83.12% and a speed of 0.014 ms on NVIDIA RTX 2060, demonstrating its effectiveness in rapid detection. Furthermore, when compared to various existing methods including Faster Region-based Convolutional Neural Network (Faster R-CNN), Single Shot Multibox Detector (SSD), EfficientDet and others, our method Attention based Feature Fusion Single Shot Multibox Detector (AFFSSD) stands out with the highest mean Average Precision (mAP) and faster speed, ranking only below You Only Look Once version 7 (YOLOv7). Conclusions This network excels in extracting regional features from images while retaining the spatial structure, facilitating the rapid detection of medical images. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
1471-2342 |
| Relation: |
https://doaj.org/toc/1471-2342 |
| DOI: |
10.1186/s12880-024-01336-y |
| URL الوصول: |
https://doaj.org/article/3dc70460304c4253bfc8ddb073bf3dc5 |
| رقم الانضمام: |
edsdoj.3dc70460304c4253bfc8ddb073bf3dc5 |
| قاعدة البيانات: |
Directory of Open Access Journals |
Full Text Finder