Automatic and Accurate Diagnosis of Alzheimer's Disease from MRI Images Improved by Deep Convolutional Neural Network

Introduction: Identifying and diagnosing Alzheimer's disease in brain tissue is one of the serious challenges of identification in the field of medical image processing. Currently, MRI is the most common way to diagnose Alzheimer's among the imaging methods. Failure to correctly identify the involved tissue can lead to misdiagnosis as healthy brain tissue. Deep learning algorithm extracts useful information as a process of detecting relevant features. In this research, we decided to use convolutional neural network in processing medical images so that we can perform diagnosis with better accuracy compared to previous works. Method: Using the designed convolutional neural network, the features of MRI T1 images have been extracted. Alzheimer's images have been analyzed using Matlab2023a software and the intended outputs have been obtained. Results: Brain Alzheimer's T1 images have been analyzed after pre -processing and entering the designed deep neural network, and in the output of the proposed algorithm, the identification accuracy and identification speed of the algorithm with the improvement of super parameters were higher compared to other common methods, which accuracy was 96% and 100% sensitivity in identification. Conclusion: The purpose of the deep learning model is to make image data with large dimensions and a large number in an understandable form for machines. It is expected that in the future feature extraction will be done more accurately and more details will be available to machine vision systems to recognize objects in the image .