Brain stroke occurs due to blockage or rupture in the cerebral blood supply and represents a critical medical emergency requiring rapid and accurate diagnosis. However, manual interpretation of CT scans is time-consuming and may delay clinical decision-making. To address this challenge, this study proposes the Deep Neural Brain Stroke Detection (DNBSD) system, a lightweight deep learning–based framework for automated stroke detection from CT images. The proposed model employs a task-specific convolutional neural network (CNN) architecture consisting of Conv2D, MaxPooling, Batch Normalization, Flatten and Dense layers, containing only 1. 67 million trainable parameters and a computational complexity of 0. 2973 GFLOPs, making it suitable for resource-constrained clinical environments. Additionally, preprocessing techniques including image resizing and normalization were applied to optimize performance. The model is trained and evaluated on two publicly available datasets: Brain Stroke CT Image Dataset (BSCI) and Brain Stroke Prediction CT Scan Image Dataset (BSPCSI), each divided into training, validation, and testing subsets. Experimental results demonstrate that the DNBSD system achieves high performance, with an accuracy of 99. 20\% and an AUC of 99. 96\% on the BSCI dataset, and an accuracy of 99. 31\% with an AUC of 99. 97\% on the BSPCSI dataset, while showing improved performance compared with several baseline approaches and state-of-the-art deep learning models. To enhance interpretability and support clinical decision-making, explainable artificial intelligence techniques, including LIME and Grad-CAM, are integrated to highlight critical regions influencing predictions. Additionally, a web-based diagnostic tool is developed to enable real-time stroke prediction. The findings suggest that the proposed approach can serve as an effective and interpretable tool for automated stroke detection, with potential to enhance clinical diagnostic workflows.
Alom et al. (Thu,) studied this question.