CNN-MobilenetV2- deep learning-based Alzheimer's disease prediction and classification

Alzheimers disease (AD) is a long-lasting brain disorder for which there is no effective treatment. Yet early detection can delay he growth of the disease. Due to the varied nature of medical tests, manual comparison, visualization and analysis of data can be time-consuming as well as demanding. As a result, an effective method for categorization of Magnetic Resonance Imaging (MRI) images is helpful but extremely difficult. In this paper, the stages of AD are identified using a unique method that effectively classifies brain MRI images using label propagation by involving a Deep Learning (DL)-based framework. Decreased brain tissue volume in brain lobes, hippocampus area, and thalamus are the primary features that aid in differentiating an AD from a normal MRI. The features should be efficient in distinguishing the characteristics between an AD-affected brain and a normal one. A Particle swarm optimization (PSO) based Speed-Up Robust Features (SURF) framework that embeds feature vectors in a subspace to maximize utilization of features that were extracted is presented. A classification method is employed in the newly generated space to categorize data into three classes namely, Normal Condition (NC), MCI, and AD using Convolution Neural Network (CNN)-MobileNetV2. The proposed scheme offers a classification accuracy is 97 yielding a 3 reduced error rate when compared to the best available approaches.

Text CNN-MobilenetV2- deep learning-based Alzheimers disease prediction and classification.pdf - Published Version Download (1MB) Official URL or Download Paper: https://www.jatit.org/volumes/Vol101No9/32Vol101No...

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