Stacked deep model‐based classification of the multiclass brain hemorrhages in CT scans

Abstract

AbstractIntracranial hemorrhage (ICH) brain hemorrhages are life‐threatening medical conditions that require a prompt and accurate diagnosis to ensure timely intervention and treatment. Computed tomography (CT) scans play a vital role in diagnosing brain hemorrhages due to their ability to provide detailed cross‐sectional images of the brain. However, the manual interpretation of CT scans is time‐consuming and subjects to human error. To address these challenges, this study proposes a stacked deep model‐based classification approach for the automated identification and differentiation of multiclass brain hemorrhages in CT scans. The stacked deep model architecture consists of multiple layers of neural networks, each designed to learn specific levels of features, leading to enhanced representation learning and improved classification performance. Later, the stacked models are connected with the weighted approach that helps to get the ensemble models in the proper order based on their power of predicting correct samples. To build the stacked deep model, a large dataset of 7,52 000 annotated CT scans is utilized to train the model and optimize its parameters. Furthermore, transfer learning is employed to leverage pretrained models for feature extraction, which aids in handling limited data scenarios. Experimental results demonstrate that the stacked deep model achieves superior accuracy (98.56%), and other classification performance measures such as sensitivity (95.89%), specificity (99.58%), AUC‐ROC (98.47%), and weighted log loss (0.04967) as compared to conventional methods. The automated classification of multiclass brain hemorrhages in CT scans using the proposed model not only significantly reduces the time required for diagnosis but also enhances the reliability and consistency of results. This model has the potential to assist medical professionals in making more informed decisions and improve patient outcomes by enabling rapid and accurate diagnosis of brain hemorrhages.