Detection and Segmentation of Subdural Hemorrhage on Head CT Images

In today's world, there has been a significant increase in the diversity of data sources and the volume of data. This situation especially necessitates the use of technologies such as deep learning in data processing. This study thoroughly examines the processing of computed tomography (CT) images with deep learning models and their role in the diagnosis of brain hemorrhages, proposing an innovative deep learning-based model for accurately detecting and segmenting brain hemorrhages. This model combines the architectures of Mask Scoring R-CNN and EfficientNet-B2, offering an effective approach for the detection and classification of brain hemorrhages. MS R-CNN is used to detect potential hemorrhage areas in CT images, while the EfficientNet-B2 architecture serves a classification function to determine whether these areas indeed contain hemorrhages. Thus, the model offers a two-stage verification process that enhances accuracy and precision. The performance of the model has been evaluated under patient-based and random partitioning techniques using by employing two distinct datasets: an open-access and a private. In patient-based evaluation, the proposed model has an accuracy of %91.59 on open dataset and an accuracy of %90.46 on private dataset for SDH hemorrhages. In the random partitioning method, the model's accuracy rate has risen to %94.30 on open dataset and %97.33 on private dataset. Compared with similar studies in the literature, these results demonstrate that the model has a high level of accuracy and reliability. This study highlights the potential and importance of AI-supported methods in the detection of brain hemorrhages and provides a solid foundation for future work in this area. Additionally, the results obtained from an open dataset by the proposed model offer a realistic and comparable reference for future work in this field. The results obtained from a second data set also clearly demonstrate the validity of the model.