Classifying Alzheimer's disease based on a convolutional neural network with MRI images

Abstract

Alzheimer's disease is a significant disease that negatively affects daily life and reduces the quality of human life. Dementia and Alzheimer's disease occur as the loss of neurons or a decrease in the relationship between neurons. So far, no effective drug has been found in diagnosing this disease. For this reason, it has become essential for individuals to diagnose the disease early and to detect the disease before it progresses. However, early diagnosis of the disease is challenging. The disease can be diagnosed after significant and irreversible effects on humans occur. A lot of research has been done worldwide for early disease diagnosis. Deep learning algorithms have become essential in diagnosing this disease. Significant progress has been made in diagnosing the disease with models created using deep learning algorithms. This study used a sequential model, conv2D, maxPooling2D, and dense layers to diagnose and classify. According to the dataset from Kaggle, a 4-class dataset has been used in this study to diagnose Alzheimer's disease. According to the Alzheimer's MRI dataset, the disease has been classified as nondemented, moderate demented, mild demented, and very mild demented, respectively. The proposed model has been trained using CNN. The number of layers and dropout rate have been used as performance metrics. In our study, activation Leaky ReLU was used. The SMOTE technique has been used to oversample the available data. This study's classification results will help experts make the right decisions. With F1Score, accuracy, recall, and precision values, 96.35% success was achieved in the CNN model. Different CNN methods can be used to advance these studies.