基于深度学习的阿尔茨海默病分类(使用医学图像

Hugo Vega-Huerta, Kevin Renzo Pantoja-Pimentel, Sebastian Yimmy Quintanilla-Jaimes, G. Maquen-Niño, Percy De-La-Cruz-VdV, Luis Guerra-Grados
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引用次数: 0

摘要

神经退行性疾病,尤其是阿尔茨海默氏症,对全球健康构成了日益严峻的挑战。这些疾病以大脑神经元退化为标志,导致神经细胞逐渐衰退。全球有 5500 多万人患有痴呆症,其中阿尔茨海默氏症对老龄人口的影响尤为突出。早期发现阿尔茨海默氏症的主要障碍是人们普遍缺乏认识。我们的主要目标是利用深度学习(DL)设计并实现一个人工智能系统,通过医学影像检测阿尔茨海默病(AD),并将其分为不同阶段,如非痴呆、中度痴呆、轻度痴呆和极轻度痴呆。数据集包含 6400 张 .jpg 格式的磁共振图像,标准化尺寸为 176 × 208 像素。为了证明数据增强和转换技术的优势,我们创建了四个场景:两个不使用这些技术,但使用了 Adam 和 SGD 优化器;两个使用了这些技术,但也分别使用了 Adam 和 SGD 优化器。主要结果显示,使用这些技术的方案在使用新数据集进行验证时表现出更稳定的性能。方案 3 采用 Adam 优化器,加权平均准确率达到 91.83%,而方案 4 采用 SGD 优化器,准确率达到 87.58%。相比之下,省略了这些技术的方案 1 和方案 2 的准确率较低,低于 55%。由此得出结论,使用准确率超过 90% 的 DL 模型对 AD 进行分类是可行的。这说明了利用数据增强和转换技术提高对输入图像变化的通用性的重要性,而这正是医疗保健领域的一个一贯因素。
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Classification of Alzheimer’s Disease Based on Deep Learning Using Medical Images
Neurodegenerative disorders, notably Alzheimer’s, pose an escalating global health challenge. Marked by the degeneration of brain neurons, these conditions lead to a gradual decline in nerve cells. Worldwide, over 55 million people grapple with dementia, with Alzheimer’s prominently impacting the aging demographic. The primary hurdle to early Alzheimer’s detection is the widespread lack of awareness. The main goal is to design and implement an artificial intelligence system using deep learning (DL) to detect Alzheimer’s disease (AD) through medical images and classify them into various stages, such as non-demented, moderate dementia, mild dementia, and very mild dementia. The dataset contains 6400 magnetic resonance images in .jpg format, with standardized dimensions of 176 × 208 pixels. To demonstrate the advantages of data augmentation and transformation techniques, four scenarios were created: two without these techniques, utilizing the Adam and SGD optimizers, and two with these techniques, also employing the Adam and SGD optimizers, respectively. The main results revealed that scenarios utilizing these techniques exhibited more stable performance when validated with a new dataset. Scenario 3, using the Adam optimizer, achieved a weighted average accuracy of 91.83%, whereas scenario 4, employing the SGD optimizer, reached 87.58% accuracy. In contrast, scenarios 1 and 2, which omitted these techniques, obtained low accuracies below 55%. It is concluded that classifying AD with a DL model exceeding 90% accuracy is feasible. This is the importance of utilizing data augmentation and transformation techniques to improve generalizability to input image variations, which is a consistent factor in the healthcare sector.
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