Alfayez Fayez, Rozov Sergey, El Tokhy Mohamed S
Department of Computer Science and Information, College of Science, Majmaah Univesity, Al Majma'ah 11952, Saudi Arabia,
Joint Institute for Nuclear Research, 141980 Dubna, Russiac.
Cell Physiol Biochem. 2024 Dec 19;58(6):739-755. doi: 10.33594/000000746.
BACKGROUND/AIMS: Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that severely affects cognitive functions and memory. Early detection is crucial for timely intervention and improved patient outcomes. However, traditional diagnostic tools, such as MRI and PET scans, are costly and less accessible. This study aims to develop an automated, cost-effective digital diagnostic approach using deep learning (DL) and computer-aided detection (CAD) methods for early AD identification and classification.
The proposed framework utilizes pretrained convolutional neural networks (CNNs) for feature extraction, integrated with two classifiers: multi-class support vector machine (MSVM) and artificial neural network (ANN). A dataset categorized into four groups-non-demented, very mild demented, mild demented, and moderate demented-was employed for evaluation. To optimize the classification process, a texture-based algorithm was applied for feature reduction, enhancing computational efficiency and reducing processing time.
The system demonstrated high statistical performance, achieving an accuracy of 91%, precision of 95%, and recall of 90%. Among the initial set of twenty-two texture features, seven were identified as particularly effective in differentiating normal cases from mild AD stages, significantly streamlining the classification process. These results validate the robustness and efficacy of the proposed DL-based CAD system.
This study presents a reliable and affordable solution for early AD detection and diagnosis. The proposed system outperforms existing state-of-the-art models and offers a valuable tool for timely treatment planning. Future research should explore its application to larger, more diverse datasets and investigate integration with other imaging modalities, such as MRI, to further enhance diagnostic precision.
背景/目的：阿尔茨海默病（AD）是一种进行性神经退行性疾病，严重影响认知功能和记忆。早期检测对于及时干预和改善患者预后至关重要。然而，传统的诊断工具，如MRI和PET扫描，成本高昂且难以普及。本研究旨在开发一种使用深度学习（DL）和计算机辅助检测（CAD）方法的自动化、经济高效的数字诊断方法，用于早期AD的识别和分类。
所提出的框架利用预训练的卷积神经网络（CNN）进行特征提取，并与两个分类器集成：多类支持向量机（MSVM）和人工神经网络（ANN）。使用一个分为四组的数据集进行评估，这四组分别为非痴呆、极轻度痴呆、轻度痴呆和中度痴呆。为了优化分类过程，应用了一种基于纹理的算法进行特征约简，提高计算效率并减少处理时间。
该系统表现出较高的统计性能，准确率达到91%，精确率为95%，召回率为90%。在最初的22个纹理特征中，有7个被确定在区分正常病例和轻度AD阶段方面特别有效，显著简化了分类过程。这些结果验证了所提出的基于DL的CAD系统的稳健性和有效性。
本研究为早期AD检测和诊断提供了一种可靠且经济实惠的解决方案。所提出的系统优于现有的先进模型，并为及时的治疗规划提供了一个有价值的工具。未来的研究应探索其在更大、更多样化数据集上的应用，并研究与其他成像模态（如MRI）的整合，以进一步提高诊断精度。
