| Abstrakt: |
The major goal of this work is to explore the performance of convolutional neural networks (CNNs) in identifying patterns symptomatic of Alzheimer's disease. This approach proposes a novel way for evaluating MRI brain images that employs convolutional neural networks (CNNs). By scanning through whole images using a convolutional neural network (CNN), attributes of equivalent spatial quality were discovered. This network employs a number of filters to examine visual data pieces. This research is based on the premise that convolutional neural networks (CNNs), with their layered architecture and filtering processes, are capable of quickly recognizing complicated patterns in medical images. This paper's CNN model employs a number of activation functions, including sigmoid, tanh, and rectified linear units (ReLU). The CNN model is divided into multiple tiers. Prior to considering totally connected layers, the network architecture featured convolution processes, pooling to reduce picture sizes, and flattening. These measures were meant to be taken. To avoid overfitting and increase the network's capacity to handle a wide range of data inputs, dropout methods were applied. The CNN model was trained and validated using the OASIS dataset's 15,200 axial MRI slices. Python 3.6 and the Keras library were used for training and validation. The model outperformed traditional approaches like KNN, SVM, and LDA in terms of sensitivity, precision, and AUC, achieving a 98% accuracy rate. As shown the design effectively established a balance between classification and feature extraction efficiency. The study's findings suggest that a customized CNN design, suitable parameter tuning, and dropout implementation might all significantly increase the accuracy of MRI-based Alzheimer's disease detection. These findings demonstrate the use of deep learning in medical image processing, where it may aid in accurate and rapid patient diagnosis. The CNN model's prediction reliability is enhanced by the fact that the research included patients of various ages, implying that the model is applicable to a wide range of ages. [ABSTRACT FROM AUTHOR] |
