College - Author 1

College of Engineering

Department - Author 1

Electrical Engineering Department

Degree Name - Author 1

BS in Electrical Engineering

College - Author 2

College of Engineering

Department - Author 2

Electrical Engineering Department

Degree - Author 2

BS in Electrical Engineering



Primary Advisor

Xiao-Hua Yu, College of Engineering, Electrical Engineering Department


This project aims to use artificial neural networks (ANN) in order to detect Alzheimer’s disease. More specifically, convolutional neural networks (CNN) will be utilized as this is the most common ANN and has been used in many different image processing applications. The purpose of using artificial neural networks as a detect method is so that an intelligent way for image and signal analysis can be used. A software that implements CNN will be developed so that users in medical settings can utilize this software to detect Alzheimer’s in patients. The input for this software will be the patient’s MRI scans. In addition, this is a project that is relevant with the current trends of an increase in development surrounding artificial intelligence. As technology has become more advanced, there has been an increase in medical developments as well. From the simulation, the hyperbolic tangent activation function provided the best results. The performance resulting from the two classifications when using the hyperbolic tangent function, on average was validation best accuracy of 81.10%, validation stopped tuning accuracy of 81.10%, training best accuracy of 100.00%, testing best accuracy of 68.94%, F-1 score of 70.06%, precision of 71.00%, and recall of 70.06%. This project will open doors to more applications of this detection method. More diseases other than Alzheimer’s disease can utilize artificial neural networks (ANN) to detect diseases early on so that lives can be restored and saved.