Brain stroke is a leading cause of mortality and long-term disability worldwide, underscoring the need for precise and rapid prediction techniques. Computed Tomography (CT) scan is considered one of the most effective methods for diagnosing brain strokes. Most stroke classification techniques use a single slice-level prediction mechanism, requiring radiologists to manually select the most critical CT slice from the original CT volume. Although clinical evaluations are often used in traditional diagnostic procedures, machine learning (ML) has opened up new avenues for improving stroke diagnosis. To supplement traditional diagnostic techniques, this study investigates machine learning models for early brain stroke prediction using CT scan images. This research proposes a novel machine learning approach to brain stroke detection, focusing on optimizing classification performance with pre-trained deep learning models and advanced optimization strategies. Pre-trained models, including DenseNet201, InceptionV3, MobileNetV2, ResNet50, and Xception, are used for feature extraction. Feature engineering techniques, including BFO, PCA, and LDA, further enhance model performance. These features are then classified using machine learning algorithms, including SVC, RF, XGB, DT, LR, KNN, and GNB. Our experiments demonstrate that the combination of MobileNetV2, LDA, and SVC achieved the highest classification accuracy of 97.93%, significantly outperforming other model-optimizer-classifier combinations. The results underline the effectiveness of integrating lightweight pre-trained models with robust optimization and classification techniques for brain stroke diagnosis.

翻译：脑卒中是全球范围内导致死亡和长期残疾的主要原因，这凸显了对精确快速预测技术的迫切需求。计算机断层扫描（CT）被认为是诊断脑卒中最有效的方法之一。大多数卒中分类技术采用单切片级预测机制，要求放射科医生从原始CT体积中手动选择最关键CT切片。尽管传统诊断流程常依赖临床评估，但机器学习（ML）为改进卒中诊断开辟了新途径。为补充传统诊断技术，本研究探讨了利用CT扫描图像进行早期脑卒中预测的机器学习模型。本研究提出了一种新颖的机器学习方法用于脑卒中检测，重点通过预训练深度学习模型和先进优化策略优化分类性能。采用包括DenseNet201、InceptionV3、MobileNetV2、ResNet50和Xception在内的预训练模型进行特征提取。通过BFO、PCA和LDA等特征工程技术进一步提升模型性能。随后使用包括SVC、RF、XGB、DT、LR、KNN和GNB在内的机器学习算法对这些特征进行分类。实验表明，MobileNetV2、LDA与SVC的组合取得了97.93%的最高分类准确率，显著优于其他模型-优化器-分类器组合。结果证实了将轻量级预训练模型与稳健的优化及分类技术相结合在脑卒中诊断中的有效性。