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AlzhiNet: Traversing from 2DCNN to 3DCNN, Towards Early Detection and Diagnosis of Alzheimer's Disease

2024-10-03 17:37:18
Romoke Grace Akindele, Samuel Adebayo, Paul Shekonya Kanda, Ming Yu

Abstract

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with increasing prevalence among the aging population, necessitating early and accurate diagnosis for effective disease management. In this study, we present a novel hybrid deep learning framework that integrates both 2D Convolutional Neural Networks (2D-CNN) and 3D Convolutional Neural Networks (3D-CNN), along with a custom loss function and volumetric data augmentation, to enhance feature extraction and improve classification performance in AD diagnosis. According to extensive experiments, AlzhiNet outperforms standalone 2D and 3D models, highlighting the importance of combining these complementary representations of data. The depth and quality of 3D volumes derived from the augmented 2D slices also significantly influence the model's performance. The results indicate that carefully selecting weighting factors in hybrid predictions is imperative for achieving optimal results. Our framework has been validated on the Magnetic Resonance Imaging (MRI) from Kaggle and MIRIAD datasets, obtaining accuracies of 98.9% and 99.99%, respectively, with an AUC of 100%. Furthermore, AlzhiNet was studied under a variety of perturbation scenarios on the Alzheimer's Kaggle dataset, including Gaussian noise, brightness, contrast, salt and pepper noise, color jitter, and occlusion. The results obtained show that AlzhiNet is more robust to perturbations than ResNet-18, making it an excellent choice for real-world applications. This approach represents a promising advancement in the early diagnosis and treatment planning for Alzheimer's disease.

Abstract (translated)

阿尔茨海默病(AD)是一种进行性的神经退行性疾病,其发病率在老年人群中的趋势不断增加,因此需要早期和准确的诊断以实现有效的疾病管理。在这项研究中,我们提出了一个新颖的混合深度学习框架,将2D卷积神经网络(2D-CNN)和3D卷积神经网络(3D-CNN)相结合,并包括自定义损失函数和体积数据增强,以增强特征提取和提高AD诊断的分类性能。根据广泛的实验,AlzhiNet超越了单独的2D和3D模型,突出了数据互补表示的重要性。从增强的2D切片中获得的3D体积的深度和质量也会显著影响模型的性能。结果表明,在混合预测中精心选择权重因子是实现最佳结果的必要条件。我们的框架已在Kaggle和MIRIAD数据集上的Magnetic Resonance Imaging(MRI)上进行了验证,获得了98.9%和99.99%的准确率, respectively,以及100%的AUC。此外,AlzhiNet还在AlzhiKaggle数据集上研究了各种扰动情景,包括高斯噪声、亮度、对比度、盐和胡椒噪声、颜色闪烁和遮挡。获得的结果表明,AlzhiNet对扰动的鲁棒性比ResNet-18更高,因此在实际应用中它是优秀的选择。这种方法在阿尔茨海默病的早期诊断和治疗规划中取得了有益的进展。

URL

https://arxiv.org/abs/2410.02714

PDF

https://arxiv.org/pdf/2410.02714.pdf


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