Detecting the Stages of Alzheimer's Disease with Pre-trained Deep Learning Architectures

Deep learning algorithms have begun to be used in medical image processing studies, especially in the last decade. MRI is used in the diagnosis of Alzheimer's disease, a type of dementia disease, which is the 7th among the diseases that cause death in the world. Alzheimer's disease has no known cure in the literature, so it is important to attempt treatment before starting the irreversible path by diagnosing the pre-illness stages. In this study, the previous stages of Alzheimer's disease were classified as normal, mild cognitive impairment, and Alzheimer's disease through brain MRIs. Different models using CNN architecture were used to classify 2182 image objects obtained from the ADNI database. The study was presented in a very comprehensive comparison framework, and the performances of 29 different pre-trained models on images were evaluated. The accuracy values of each model and the precision, specificity, and sensitivity rates of each class were determined. In the study, the EfficientNetB0 model provided the highest accuracy at the test stage with an accuracy rate of 92.98%. In the comparative evaluation stage with the confusion matrix, the highest rates of precision, sensitivity, and specificity values of the Alzheimer's disease class were achieved by EfficientNetB3 (89.78%), EfficientNetB2 (94.42%), and EfficientNetB3 (97.28%) models, respectively. The results of the study showed that among the pre-trained models, EfficientNet models achieved a high rate of classification performance as the models with the highest performance. This study will contribute to clinical studies in early prevention by detecting Alzheimer's disease before it occurs.