چکیده مقاله
In recent years, the global increase in life expectancy has highlighted the importance of diagnosing Alzheimer's disease AD The onset of mild cognitive impairment MCI can indicate a progression towards irreversible mental decline, including AD and dementia Consequently, early detection of MCI has become a focal point for researchers, as intervening at this stage can halt its advancement and facilitate effective treatment Traditionally, biochemical and psychological tests have been used for diagnosis However, an emerging approach involves analyzing Magnetic Resonance Imaging MRI scans to detect structural changes in the brain associated with AD In this study, brain MRI images are pre processed using the Statistical Parametric Mapping SPM toolbox Subsequently, the gray matter GM is segmented and input into a ConvolutionalNeural Network CNN for analysis The Alzheimer's Disease Neuroimaging Initiative ADNI dataset is utilized for this purpose The results demonstrate that the proposed method achieves an accuracy exceeding 95% in classifying three categories: Normal Control NC , AD, and MCI This underscores the potential of MRI based approaches forearly and accurate diagnosis of AD, paving the way for timely interventions and improved patient outcomes
کلیدواژهها
نویسندگان
Sara Yousefi Javan
Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
Mahboobeh Houshmand
Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
Seyyed Abed Hosseini
Department of Electrical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
شیوه ارجاع
Yousefi Javan, Sara and Houshmand, Mahboobeh and Hosseini, Seyyed Abed,1403,Diagnosis of Mild Cognitive Impairment (MCI) Using a CNN-LSTM Hybrid Algorithm,The 22th National Conference on Computer Science and Engineering and Information Technology,Babol
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