Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, 223003, China.
Department of Gastroenterology, The Second People's Hospital of Huai'an, The Affiliated Huai'an Hospital of Xuzhou Medical University, Huaian, 223002, China.
Sci Rep. 2024 Oct 8;14(1):23382. doi: 10.1038/s41598-024-74609-9.
The rise of object detection models has brought new breakthroughs to the development of clinical decision support systems. However, in the field of gastrointestinal polyp detection, there are still challenges such as uncertainty in polyp identification and inadequate coping with polyp scale variations. To address these challenges, this paper proposes a novel gastrointestinal polyp object detection model. The model can automatically identify polyp regions in gastrointestinal images and accurately label them. In terms of design, the model integrates multi-channel information to enhance the ability and robustness of channel feature expression, thus better coping with the complexity of polyp structures. At the same time, a hierarchical structure is constructed in the model to enhance the model's adaptability to multi-scale targets, effectively addressing the problem of large-scale variations in polyps. Furthermore, a channel attention mechanism is designed in the model to improve the accuracy of target positioning and reduce uncertainty in diagnosis. By integrating these strategies, the proposed gastrointestinal polyp object detection model can achieve accurate polyp detection, providing clinicians with reliable and valuable references. Experimental results show that the model exhibits superior performance in gastrointestinal polyp detection, which helps improve the diagnostic level of digestive system diseases and provides useful references for related research fields.
目标检测模型的兴起为临床决策支持系统的发展带来了新的突破。然而，在胃肠道息肉检测领域，仍然存在息肉识别的不确定性以及对息肉尺度变化的处理不足等挑战。针对这些挑战，本文提出了一种新颖的胃肠道息肉目标检测模型。该模型能够自动识别胃肠道图像中的息肉区域，并进行准确标注。在设计方面，该模型集成了多通道信息，增强了通道特征表达的能力和鲁棒性，从而更好地应对息肉结构的复杂性。同时，模型构建了层次结构，增强了模型对多尺度目标的适应能力，有效解决了息肉尺度变化较大的问题。此外，模型中设计了通道注意力机制，提高了目标定位的准确性，减少了诊断的不确定性。通过整合这些策略，所提出的胃肠道息肉目标检测模型能够实现准确的息肉检测，为临床医生提供可靠且有价值的参考。实验结果表明，该模型在胃肠道息肉检测中表现出优异的性能，有助于提高消化系统疾病的诊断水平，并为相关研究领域提供有益的参考。
