A Computer-Aided Detection to Intracranial Hemorrhage by Using Deep Learning: A Case Study

Intracranial hemorrhage (ICH) is an acute-stroke type leading to a high mortality rate. ICH diagnoses can keep patients out of life-threatening and longlasting consequences. These diagnoses are typically detected by analyzing images of Computed Tomography (CT) scan and symptoms of the patients. Consequently, the expertise of radiologists is significant in terms of detecting signals of ICH and making in-time decisions to save these patients. Over the past few decades, due to powerful-and-modern machine learning algorithms, several computer-aided detection (CAD) systems have been developed. They are becoming an effective part of routine clinical work to detect a few critical diseases, such as strokes, cancers, and so on. In this paper, we introduce a CAD that combines a deep-learning model and typical image processing techniques to determine whether patients suffer from ICH due to their CT images. The deep-learning model based on MobileNetV2 architecture was trained on RSNA (Radiological Society of North America) Intracranial Hemorrhage dataset. Then it was validated on a dataset of ICH-Vietnamese cases collected from Vinh Long Province Hospital, Vietnam. The experiment indicated that the classifier achieved AUC of 0.991, sensitivity of 0.992, and specificity of 0.807. After the deep-learning model identifies ICH-suspected slices, the Hounsfield Unit (HU) method is employed to highlight specific hemorrhage areas in these slices. DBSCAN is also used to remove noises detected by the HU method.