Multi-classification for Predicting Alzheimer's Disease Using 1.5T1-weighted MRI Imaging and Deep Learning 2D CNN - Shahad Shahad Haitham Ali (Journal of Technology and System Information, 2025)
Medical imaging holds the pivotal role in clinical diagnosis, education, research work, and treatment of medicine. Medical professionals are tasked with analyzing and interpreting high-level medical data, which is extremely difficult in nature due to the intricate nature of medical images. Deep learning techniques are emerging as strong tools, yielding promising and correct results in the analysis of medical data. Alzheimer's disease, which affects one in every ten individuals aged 65 and above, is a central focus where such advancements are aimed. Artificial intelligence has proved capable of distinguishing between healthy brains and Alzheimer's disease-affected brains. The etiology of the disease lies in abnormal proteins accumulating inside and outside neuronal cells, causing irreparable loss of memory. Alzheimer's disease is the most prevalent form of dementia, and "Mild Cognitive Impairment" (MCI) typically presents as an early indicator, identifying patients who are at higher risk of Alzheimer's disease. However, not all MCI patients go on to develop Alzheimer's, and this highlights the importance of effective interventions. While some patients with MCI (MCI-nc) remain stable, others will progress to Alzheimer's disease. Here, a CNN model was designed and trained first with four classifiers and later retrained with five classifiers. The accuracy rate of the four-classifier model was 98%, while that of the five-classifier model was slightly higher with an accuracy of 98.67%.