Utilization of a convolutional method for Alzheimer disease diagnosis

With the increasing number of cases as well as care costs, Alzheimer’s disease has gained more interest in several scientific communities especially medical and computer science. Clinical and analytical tests are widely accepted techniques for detecting Alzheimer cases. However, early detection can help prevent damage to brain tissue and heal it with proper treatment. Interpreting brain images is considered as a time-consuming task with a high error-prone. Recently, advanced machine learning methods have successfully proved high performance in various fields including brain image analysis. These existing techniques, which become more used for clinical disease detection, present challenging wrongness sensibility to detect aberrant values or areas in the human brain. We conducted our work to automate the detection of the damaged areas and diagnose Alzheimer’s disease. Our method can segment MRI images, identify brain lesions and the different stages of Alzheimer’s disease. We evaluated our method using ample cases form public databases to demonstrate that our proposition performed reliable and effective results. Our proposal achieved an accuracy of 94.73%, a recall rate of 93.82%, and an F1-score of 92.8%. Also, the detection precision reached 91.76% with a sensitivity of 92.48% and a specificity rate of 90.64%. Our method creates an important way to optimize the imaging process via an automated computer-assisted diagnosis using potential deep learning methods to increase the consistency and accuracy of Alzheimer’s disease diagnosis worldwide.