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.

翻译：医学图像的精确分类对于检测胃肠道异常至关重要，在此领域中分类错误可能严重影响患者预后。我们提出一种基于集成的方法，以提高分析复杂图像数据集时的诊断准确性。通过结合卷积块注意力模块与深度神经网络，我们利用每种模型独特的特征提取能力来提升整体精度。同时引入随机森林、XGBoost、支持向量机和K近邻等分类模型，以进一步增强所提集成方法的预测多样性。采用这些方法后，所提出的CAVE-Net框架展现出鲁棒的特征判别能力和改进的分类结果。实验评估表明，CAVE-Net在具有挑战性且类别不平衡的数据集上实现了高精度与强鲁棒性，为计算机视觉任务的更广泛应用展现出显著潜力。