Ischemic Stroke Classification Using VGG-16 Convolutional Neural Networks: A Study on Moroccan MRI Scans

Wafae Abbaoui, Sara Retal, Soumia Ziti, Brahim El Bhiri, Hassan Moussif
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Abstract

This study presents a comprehensive exploration of deep learning models for precise brain ischemic stroke classification using medical data from Morocco. Following the OSEMN approach, our methodology leverages transfer learning with the VGG-16 architecture and employs data augmentation techniques to enhance model performance. Our developed model achieved a remarkable validation accuracy of 90%, surpassing alternative state-of-theart models (ResNet50: 87.0%, InceptionV3: 82.0%, VGG-19: 81.0%). Notably, all models were rigorously evaluated on the same meticulously curated dataset, ensuring fair and consistent comparisons. The investigation underscores VGG-16’s superior performance in distinguishing stroke cases, highlighting its potential as a robust tool for accurate diagnosis. Comparative analyses among popular deep learning architectures not only demonstrate our model’s efficacy but also emphasize the importance of selecting the right architecture for medical image classification tasks. These findings contribute to the growing evidence supporting advanced deep learning techniques in medical imaging. Achieving a validation accuracy of 90%, our model holds significant promise for real-world healthcare applications, showcasing the critical role of cutting-edge technologies in advancing diagnostic accuracy and the transformative potential of deep learning in the medical field.
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使用 VGG-16 卷积神经网络进行缺血性中风分类:摩洛哥核磁共振成像扫描研究
本研究利用摩洛哥的医疗数据,对用于精确脑缺血中风分类的深度学习模型进行了全面探索。按照 OSEMN 方法,我们的方法利用 VGG-16 架构的迁移学习,并采用数据增强技术来提高模型性能。我们开发的模型的验证准确率高达 90%,超过了其他先进模型(ResNet50:87.0%;InceptionV3:82.0%;VGG-19:81.0%)。值得注意的是,所有模型都在同一个精心策划的数据集上进行了严格评估,确保了比较的公平性和一致性。这项调查强调了 VGG-16 在区分中风病例方面的卓越性能,凸显了其作为准确诊断的强大工具的潜力。流行的深度学习架构之间的比较分析不仅证明了我们模型的功效,还强调了为医学图像分类任务选择正确架构的重要性。这些发现为医学影像中支持高级深度学习技术的证据越来越多做出了贡献。我们的模型达到了 90% 的验证准确率,在现实世界的医疗保健应用中大有可为,展示了前沿技术在提高诊断准确率方面的关键作用,以及深度学习在医疗领域的变革潜力。
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