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Alzheimer's Disease (AD), the most common type of dementia, is characterized by memory issues that worsen with time. When evaluating single modalities, there are specific efficient learning techniques that produce a poor identification rate. The clinical significance of Mild Cognitive Impairment (MCI) has clinical importance, which is crucial for identifying and categorizing AD patients. This study includes the utilization of 3D T1 weighted /Magnetic Resonance Imaging (3D T1WI MRI) images where the structural changes in the brain are identified for predicting early AD. The classification accuracy for AD, MCI, and Normal Control (NC) has also increased due to cognitive evaluations when comparing three one vs one classifications, i.e. AD vs. NC, MCI vs. NC, AD vs. MCI, and multi-class classification. The proposed approach's novelty is a binary and multi-class classification of structural MRI images using a Siamese 3D Convolutional Neural Network (Siamese 3D-CNN). The Stochastic Gradient Descent (SGD) has been employed to modify the proposed network's weights, and 5-Fold Cross-Validation (CV) has been used for each binary classification task. Our study used the neuropsychological data of 417 people from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to validate the suggested methodology. Experimental results demonstrate that accuracy, sensitivity, and specificity for three binary classifications produce the maximum percentage, in contrast to other relevant approaches found in the literature. In addition, the multi-class classification accuracy of 99.8% was attained. [ABSTRACT FROM AUTHOR]

This paper presents a deep learning approach to identify and classify various defects in the laser-directed energy manufactured components. It mainly focuses on the Convolutional Neural Network (CNN) architectures, such as VGG16, AlexNet, GoogLeNet and ResNet to perform the automated classification of defects. The main objectives of this research are to manufacture components using the laser-directed energy deposition process, prepare a dataset of horizontal wall structure, vertical wall structure and cuboid structure with three defective classes such as voids, flash formation, and rough textures, and one non-defective class, use this dataset with a deep learning algorithm to classify the defect and use the efficient algorithm to detect defects. The next objective is to compare the performance parameters of VGG16, AlexNet, GoogLeNet and ResNet used for classifying defects. It has been observed that the best results were obtained when the VGG16 architecture was applied to an augmented dataset. With augmentation, the VGG16 architecture gave a test accuracy of 94.7% and a precision of 80.0%. The recall value is 89.3% and an F1-Score is 89.5%. The VGG16 architecture with augmentation is highly reliable for automating the defect detection process and classifying defects in the laser additive manufactured components. [ABSTRACT FROM AUTHOR]