Classification of Remote Sensing Image Scenes Using Double Feature Extraction Hybrid Deep Learning Approach
Volume-3 | Issue-2
Light Weight CNN based Robust Image Watermarking Scheme for Security
Volume-3 | Issue-2
Principle of 6G Wireless Networks: Vision, Challenges and Applications
Volume-3 | Issue-4
PROGRESS AND PRECLUSION OF KNEE OSTEOARTHRITIS: A STUDY
Volume-3 | Issue-3
Is Internet becoming a Major Contributor for Global warming - The Online Carbon Footprint
Volume-2 | Issue-4
Augmented Reality in Education
Volume-2 | Issue-4
A Study on Various Task-Work Allocation Algorithms in Swarm Robotics
Volume-2 | Issue-2
IoT based Biotelemetry for Smart Health Care Monitoring System
Volume-2 | Issue-3
Tungsten DiSulphide FBG Sensor for Temperature Monitoring in Float Glass Manufacturing
Volume-2 | Issue-4
GUI based Industrial Monitoring and Control System
Volume-3 | Issue-2
AUTOMATION USING IOT IN GREENHOUSE ENVIRONMENT
Volume-1 | Issue-1
Principle of 6G Wireless Networks: Vision, Challenges and Applications
Volume-3 | Issue-4
Classification of Remote Sensing Image Scenes Using Double Feature Extraction Hybrid Deep Learning Approach
Volume-3 | Issue-2
Light Weight CNN based Robust Image Watermarking Scheme for Security
Volume-3 | Issue-2
VIRTUAL REALITY GAMING TECHNOLOGY FOR MENTAL STIMULATION AND THERAPY
Volume-1 | Issue-1
Design of Digital Image Watermarking Technique with Two Stage Vector Extraction in Transform Domain
Volume-3 | Issue-3
Analysis of Natural Language Processing in the FinTech Models of Mid-21st Century
Volume-4 | Issue-3
PROGRESS AND PRECLUSION OF KNEE OSTEOARTHRITIS: A STUDY
Volume-3 | Issue-3
Image Augmentation based on GAN deep learning approach with Textual Content Descriptors
Volume-3 | Issue-3
Comparative Analysis for Personality Prediction by Digital Footprints in Social Media
Volume-3 | Issue-2
Volume - 5 | Issue - 2 | june 2023
Published
19 June, 2023
Alzheimer's is a neurological condition that affects millions of individuals throughout the world. Early detection is critical for optimal treatment and management of this condition. This research presents a classification model that uses Convolutional Neural Networks (CNNs) to reliably identify the Alzheimer's disease using brain MRI scans. A publicly available dataset of brain MRI images divided into four categories: mild dementia, moderate dementia, non-dementia, and very mild dementia, has been utilized. When compared to the other classes, the number of samples for 'ModerateDemented' was found to be significantly lower, showing class imbalance. To solve this, the study oversamples the data by Synthetic Minority Over-sampling Technique (SMOTE) and generates extra samples. The proposed method augments the data utilizing the TensorFlow ImageDataGenerator. The study uses a CNN model with several sorts of normalization blocks. The suggested model achieved an accuracy of 95.2%, for the test set outperforming prior state-of-the-art techniques.
KeywordsDeep learning convolutional neural network image classification data imbalance over-sampling SMOTE TensorFlow
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