Intracranial hemorrhage (ICH) refers to the leakage or accumulation of blood within the skull, which occurs due to the rupture of blood vessels in or around the brain. If this condition is not diagnosed in a timely manner and appropriately treated, it can lead to serious complications such as decreased consciousness, permanent neurological disabilities, or even death.The primary aim of this study is to detect the occurrence or non-occurrence of ICH, followed by determining the type of subdural hemorrhage (SDH). These tasks are framed as two separate binary classification problems. By adding two layers to the co-scale convolutional attention (CCA) classifier architecture, we introduce a novel approach for ICH detection. In the first layer, after extracting features from different slices of computed tomography (CT) scan images, we combine these features and select the 50 components that capture the highest variance in the data, considering them as informative features. We then assess the discriminative power of these features using the bootstrap forest algorithm, discarding those that lack sufficient discriminative ability between different classes. This algorithm explicitly determines the contribution of each feature to the final prediction, assisting us in developing an explainable AI model. The features feed into a boosting neural network as a latent feature space. In the second layer, we introduce a novel uncertainty-based fuzzy integral operator to fuse information from different CT scan slices. This operator, by accounting for the dependencies between consecutive slices, significantly improves detection accuracy.

翻译：颅内出血（ICH）指颅腔内血液的漏出或积聚，由大脑内部或周围血管破裂引起。若未能及时诊断并妥善治疗，该病症可能导致意识下降、永久性神经功能缺损甚至死亡等严重并发症。本研究的主要目标是检测ICH是否发生，进而确定硬膜下出血（SDH）的类型。这两项任务被构建为两个独立的二分类问题。通过在共尺度卷积注意力（CCA）分类器架构中增加两个层级，我们提出了一种新颖的ICH检测方法。在第一层级中，从计算机断层扫描（CT）图像的不同切片提取特征后，我们融合这些特征并筛选出能捕获数据最高方差的50个成分，将其视为信息特征。随后采用自助森林算法评估这些特征的判别能力，剔除那些在不同类别间缺乏足够区分度的特征。该算法能显式确定每个特征对最终预测的贡献度，有助于构建可解释的AI模型。这些特征作为潜在特征空间输入至提升神经网络。在第二层级中，我们引入了一种基于不确定性的新型模糊积分算子，用于融合来自不同CT扫描切片的信息。该算子通过考虑连续切片间的依赖关系，显著提升了检测准确率。