{"title":"Alzheimer’s Disease Prediction Using Fly-Optimized Densely Connected Convolution Neural Networks Based on MRI Images","authors":"","doi":"10.14283/jpad.2024.66","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>Alzheimer’s is a degenerative brain cell disease that affects around 5.8 million people globally. The progressive neurodegenerative disease known as Alzheimer’s Disease (AD), affects the frontal cortex, the part of the brain in charge of memory, language, and cognition. As a result, researchers are utilizing a variety of machine-learning techniques to create an automated method for AD detection. The massive data collected during ROI and biomarker identification takes longer to handle using current methods. This study uses metaheuristic-tuned deep learning to detect the AD-affected region. The research utilizes advanced deep learning and image processing techniques to enhance early and accurate diagnosis of Alzheimer’s disease, potentially enhancing patient outcomes and prompt therapy. The capacity of deep neural networks to extract complex patterns from magnetic resonance imaging (MRI) scans makes them indispensable in the diagnosis of AD since they allow the detection of minor aberrations and complex alterations in brain structure and composition. An adaptive histogram approach processes the collected photographs, and a weighted median filter is used in place of the noisy pixels. The next step is to identify the issue region using a deep convolution network-based clustering segmentation process. A correlated information theory approach is used to extract various textural and statistical features from the separated regions. Lastly, the selected features are probed by the fly-optimized densely linked convolution neural networks. The method surpasses state-of-the-art techniques in sensitivity (15.52%), specificity (15.62%), accuracy (9.01%), error rate (11.29%), and F-measure (10.52%) for recognizing AD-impacted regions in MRI scans using the Kaggle dataset.</p>","PeriodicalId":22711,"journal":{"name":"The Journal of Prevention of Alzheimer's Disease","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Journal of Prevention of Alzheimer's Disease","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14283/jpad.2024.66","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 0
Abstract
Alzheimer’s is a degenerative brain cell disease that affects around 5.8 million people globally. The progressive neurodegenerative disease known as Alzheimer’s Disease (AD), affects the frontal cortex, the part of the brain in charge of memory, language, and cognition. As a result, researchers are utilizing a variety of machine-learning techniques to create an automated method for AD detection. The massive data collected during ROI and biomarker identification takes longer to handle using current methods. This study uses metaheuristic-tuned deep learning to detect the AD-affected region. The research utilizes advanced deep learning and image processing techniques to enhance early and accurate diagnosis of Alzheimer’s disease, potentially enhancing patient outcomes and prompt therapy. The capacity of deep neural networks to extract complex patterns from magnetic resonance imaging (MRI) scans makes them indispensable in the diagnosis of AD since they allow the detection of minor aberrations and complex alterations in brain structure and composition. An adaptive histogram approach processes the collected photographs, and a weighted median filter is used in place of the noisy pixels. The next step is to identify the issue region using a deep convolution network-based clustering segmentation process. A correlated information theory approach is used to extract various textural and statistical features from the separated regions. Lastly, the selected features are probed by the fly-optimized densely linked convolution neural networks. The method surpasses state-of-the-art techniques in sensitivity (15.52%), specificity (15.62%), accuracy (9.01%), error rate (11.29%), and F-measure (10.52%) for recognizing AD-impacted regions in MRI scans using the Kaggle dataset.
摘要 阿尔茨海默氏症是一种脑细胞退化性疾病，全球约有 580 万人患有此病。被称为阿尔茨海默病（AD）的渐进性神经退行性疾病会影响大脑额叶皮层，而额叶皮层是大脑中负责记忆、语言和认知的部分。因此，研究人员正在利用各种机器学习技术来创建一种自动检测阿尔茨海默病的方法。在 ROI 和生物标记物识别过程中收集的海量数据需要更长的时间才能用现有方法处理。本研究利用元启发式调整的深度学习来检测注意力缺失症的影响区域。该研究利用先进的深度学习和图像处理技术来提高阿尔茨海默病的早期准确诊断率，从而改善患者的预后并及时进行治疗。深度神经网络能够从磁共振成像（MRI）扫描中提取复杂的模式，这使它们成为诊断阿尔茨海默病不可或缺的工具，因为它们能够检测大脑结构和组成中的微小畸变和复杂变化。自适应直方图方法处理收集到的照片，并使用加权中值滤波器代替噪声像素。下一步是使用基于深度卷积网络的聚类分割过程来识别问题区域。使用相关信息理论方法从分离的区域中提取各种纹理和统计特征。最后，选定的特征将通过密集链接的卷积神经网络进行探测。在使用 Kaggle 数据集识别磁共振成像扫描中的注意力缺失症影响区域时，该方法在灵敏度（15.52%）、特异性（15.62%）、准确性（9.01%）、错误率（11.29%）和 F 测量（10.52%）方面均超越了最先进的技术。
期刊介绍：
The JPAD Journal of Prevention of Alzheimer’Disease will publish reviews, original research articles and short reports to improve our knowledge in the field of Alzheimer prevention including: neurosciences, biomarkers, imaging, epidemiology, public health, physical cognitive exercise, nutrition, risk and protective factors, drug development, trials design, and heath economic outcomes.JPAD will publish also the meeting abstracts from Clinical Trial on Alzheimer Disease (CTAD) and will be distributed both in paper and online version worldwide.We hope that JPAD with your contribution will play a role in the development of Alzheimer prevention.
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