An Attention-Based ResNet Architecture for Acute Hemorrhage Detection and Classification: Toward a Health 4.0 Digital Twin Study.

Due to the advancement of digital twin (DT) technology, Health 4.0 applications have become reality and starting to take roots. In this article, we focus on intracranial hemorrhage (ICH) which is a life-threatening emergency that needs immediate diagnosis and treatment. ICH is caused by bleeding inside the skull or brain. Radiologists typically examine computed tomography (CT) scans of the patients to determine the ICH and its subtype. But the manual assessment of the CT scan is a complex and time-consuming task. The existing pre-trained convolutional neural network (CNN) models are state-of-the-art for ICH classification. However, they employ poor feature extraction techniques which hinder overall model performance. Furthermore, they suffer from the curse of dimensionality and use redundant and noisy features. The problem of imbalanced data is also crucial for achieving model generalization. This paper proposes a hybrid attention-based ResNet architecture for ICH detection and classification. An attention mechanism allows the model to focus on a specific region and extract relevant features. Principal component analysis (PCA) is used for dimensionality reduction and redundant feature removal whereas deep convolutional generative adversarial network (DCGAN) is used for resolving the class imbalance problem. The proposed model is evaluated using the dataset assembled during the Radiologist Society of North America (RSNA) ICH detection challenge 2019. The results show that our proposed model outperforms existing state-of-the-art models in terms of accuracy and F1-score. ICH classification achieved accuracies of 99.2%, 97.1%, 96.7%, 96.7% and 96.1%, for detecting epidural hemorrhage (EH), intraparenchymal hemorrhage (IH), intraventricular hemorrhage (IVH), subdural hemorrhage (SH), and subarachnoid hemorrhage (SAH) subtypes respectively. The F1-score of 96.1% for EH subtype is also best when compared with the benchmark models.