With an intention of improving healthcare performance, wearable technology products utilize several digital health sensors which are classically linked into sensor networks, including body-worn and ambient sensors. On the other hand, intracerebral hemorrhage (ICH) defines the injury of blood vessels in the brain regions, which is accountable for 10–15% of strokes. X-ray computed tomography (CT) scans are commonly employed to determine the position and size of the hemorrhages. Manual segmentation of the CT scans by planimetry using a radiologist is effective; however, it consumes more time. Therefore, this paper develops deep learning (DL)–based ICH diagnosis using GrabCut-based segmentation with synergic deep learning (SDL), named GC-SDL model. The proposed method make use of Gabor filtering for noise removal, thereby the image quality can be raised. In addition, GrabCut-based segmentation technique is applied to identify the diseased portions effectively in the image. To perform the feature extraction process, SDL model is utilized and finally, softmax (SM) layer is employed as a classifier. In order to investigate the performance of the GC-SDL model, an extensive set of experimentation takes place using a benchmark ICH dataset, and the results are examined under different evaluation metrics. The experimental outcome stated that the GC-SDL model has reached a higher sensitivity of 94.01%, specificity of 97.78%, precision of 95.79%, and accuracy of 95.73%.

为了改善医疗保健性能，可穿戴技术产品采用了几种数字式健康传感器，这些传感器被经典地链接到传感器网络中，包括人体传感器和环境传感器。另一方面，脑出血（ICH）定义了脑部区域的血管损伤，占脑卒中的10-15％。X射线计算机断层扫描（CT）扫描通常用于确定出血的位置和大小。使用放射科医生通过平面测量法手动分割CT扫描是有效的；但是，它消耗更多时间。因此，本文将基于GrabCut的细分与协同深度学习（SDL）一起开发基于深度学习（DL）的ICH诊断，称为GC-SDL模型。提出的方法利用Gabor滤波去除噪声，从而可以提高图像质量。此外，基于GrabCut的分割技术可有效识别图像中的病变部分。为了执行特征提取过程，利用SDL模型，最后，将softmax（SM）层用作分类器。为了调查GC-SDL模型的性能，使用基准ICH数据集进行了广泛的实验，并在不同的评估指标下检查了结果。实验结果表明，GC-SDL模型具有更高的灵敏度，为94.01％，特异性为97.78％，精度为95.79％，准确性为95.73％。softmax（SM）层用作分类器。为了调查GC-SDL模型的性能，使用基准ICH数据集进行了广泛的实验，并在不同的评估指标下检查了结果。实验结果表明，GC-SDL模型的灵敏度更高，为94.01％，特异性为97.78％，精度为95.79％，准确性为95.73％。softmax（SM）层用作分类器。为了调查GC-SDL模型的性能，使用基准ICH数据集进行了广泛的实验，并在不同的评估指标下检查了结果。实验结果表明，GC-SDL模型的灵敏度更高，为94.01％，特异性为97.78％，精度为95.79％，准确性为95.73％。