Ischemia and Hemorrhage detection in CT images with Hyperparameter optimization of classification models and Improved UNet Segmentation Model

Abstract

Deep learning is a powerful technique that has been applied to stroke detection using medical imaging. Stroke is a medical condition that occurs when the blood supply to the brain is interrupted, which can cause brain damage and other serious complications. Stroke detection is important to minimize damage and improve patient outcomes. One of the most common imaging modalities for stroke detection is CT (Computed Tomography). CT can provide detailed images of the brain and can be used to identify the presence and location of a stroke. Deep learning models, particularly convolutional neural networks (CNNs), have shown promise for stroke detection using CT images. These models can learn to automatically identify patterns in the images that are indicative of a stroke, such as the presence of an infarct or hemorrhage. Some examples of deep learning models used for stroke detection in CT images are U-Net, which is commonly used for medical image segmentation tasks, and CNNs, which have been trained to classify brain CT images into normal or abnormal. The purpose of this study is to identify the type of stroke from brain CT images taken without the administration of a contrast agent, i.e., occlusive (ischemic) or hemorrhagic (hemorrhagic). Stroke images were collected, and a dataset was constructed with medical specialists. Deep learning classification models were evaluated with hyperparameter optimization techniques. And the result is segmented with an improved Unet model to visualize the stroke in CT images. Classification models were compared and VGG16 achieved %94 success. Unet model achieved %60 IOU and detected the ischemia and hemorrhage differences.