Toward the detection of intracranial hemorrhage: a transfer learning approach

Traumatic brain injury can cause intracranial hemorrhages, which, if not diagnosed and treated early, may lead to fatal outcomes due to excessive bleeding inside the cranium. In the United States, stroke is the fifth-leading cause of death, and approximately 10% of strokes result from intracranial hemorrhages. Identifying the presence, location, and type of hemorrhage is a critical step in treating emergency room patients. To determine the position and size of hemorrhages, X-ray computed tomography (CT) scans are commonly employed. While radiologists are highly skilled in analyzing CT scan images, the process is time-consuming. This study deals with intracranial hemorrhage detection using a deep-learning approach. First, we introduce convolutional neural networks (CNNs), a type of neural network designed for image-based datasets. Subsequently, we discuss various hyperparameter optimization techniques to enhance CNN training efficiency. As CNN training can be computationally expensive and time-intensive in many instances, we address this challenge by leveraging transfer learning with pre-trained models. We explore different transfer learning architectures, including VGGNet, AlexNet, EfficientNetB2, ResNet, MobileNet, and InceptionNet. The proposed framework for intracranial hemorrhage detection is implemented using transfer learning on the Radiological Society of North America Intracranial Hemorrhage Detection dataset, which is publicly available on Kaggle. Specifically, VGGNet is employed within this framework using powerful deep-learning libraries such as TensorFlow. This methodology can also be generalized to classify CT scan images in other biomedical domains.