CAVE-Net通过集成学习提升胶囊内镜异常分类准确率
CAVE-Net: Classifying Abnormalities in Video Capsule Endoscopy
- 融合注意力模块与多种分类器构建集成模型
- 在复杂不平衡数据上实现高准确率与强鲁棒性
- 适用于医疗图像分析与计算机视觉任务
准确分类医学图像对检测胃肠道异常至关重要，错误分类可能严重影响患者预后。本文提出一种基于集成学习的方法，以提升复杂图像数据集的诊断准确性。采用带有卷积块注意力模块的深度神经网络，结合随机森林、XGBoost、支持向量机和K近邻等分类器，增强特征区分能力与预测多样性。所提框架CAVE-Net在挑战性且类别不平衡的数据上表现优异，验证了其在计算机视觉任务中的广阔应用前景。
原文摘要 · Abstract (English)
Accurate classification of medical images is critical for detecting abnormalities in the gastrointestinal tract, a domain where misclassification can significantly impact patient outcomes. We propose an ensemble-based approach to improve diagnostic accuracy in analyzing complex image datasets. Using a Convolutional Block Attention Module along with a Deep Neural Network, we leverage the unique feature extraction capabilities of each model to enhance the overall accuracy. The classification models, such as Random Forest, XGBoost, Support Vector Machine and K-Nearest Neighbors are introduced to further diversify the predictive power of proposed ensemble. By using these methods, the proposed framework, CAVE-Net, provides robust feature discrimination and improved classification results. Experimental evaluations demonstrate that the CAVE-Net achieves high accuracy and robustness across challenging and imbalanced classes, showing significant promise for broader applications in computer vision tasks.
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