Due to the fast development of medical imaging technologies, medical image analysis has entered the period of big data for proper disease diagnosis. At the same time, intracerebral hemorrhage (ICH) becomes a serious disease which affects the injury of blood vessels in the brain regions. This paper presents an artificial intelligence and big data analytics-based ICH e-diagnosis (AIBDA-ICH) model using CT images. The presented model utilizes IoMT devices for data acquisition process. The presented AIBDA-ICH model involves graph cut-based segmentation model for identifying the affected regions in the CT images. To manage big data, Hadoop Ecosystem and its elements are mainly used. In addition, capsule network (CapsNet) model is applied as a feature extractor to derive a useful set of feature vectors. Finally, the presented AIBDA-ICH model makes use of the fuzzy deep neural network (FDNN) model to carry out classification process. For validating the superior performance of the AIBDA-ICH method, an extensive set of simulations were performed and the outcomes are examined under diverse aspects. The experimental values pointed out the improved e-diagnostic performance of the AIBDA-ICH model over the other compared methods with the precision and accuracy of 94.96% and 98.59%, respectively.

由于医学影像技术的飞速发展，医学影像分析已进入大数据时代，以正确诊断疾病。同时，脑出血（ICH）成为影响大脑区域血管损伤的严重疾病。本文提出了一种使用 CT 图像的基于人工智能和大数据分析的 ICH 电子诊断 (AIBDA-ICH) 模型。所提出的模型利用 IoMT 设备进行数据采集过程。提出的 AIBDA-ICH 模型涉及基于图切割的分割模型，用于识别 CT 图像中的受影响区域。为了管理大数据，主要使用 Hadoop 生态系统及其元素。此外，胶囊网络 (CapsNet) 模型被用作特征提取器以导出一组有用的特征向量。最后，所提出的 AIBDA-ICH 模型利用模糊深度神经网络 (FDNN) 模型进行分类过程。为了验证 AIBDA-ICH 方法的卓越性能，我们进行了大量模拟，并在不同方面检查了结果。实验值表明，AIBDA-ICH 模型的电子诊断性能优于其他比较方法，精确度和准确度分别为 94.96% 和 98.59%。