A proficient approach for the classification of Alzheimer's disease using a hybridization of machine learning and deep learning.

Alzheimer's disease (AD) is a neurodegenerative disorder. It causes progressive degeneration of the nervous system, affecting the cognitive ability of the human brain. Over the past two decades, neuroimaging data from Magnetic Resonance Imaging (MRI) scans has been increasingly used in the study of brain pathology related to the birth and growth of AD.

PCA driven mixed filter pruning for efficient convNets.

Deployment of the deep neural networks (DNNs) on resource-constrained devices is a challenging task due to their limited memory and computational power. In most cases, the pruning techniques do not prune the DNNs to full extent and redundancy still exists in these models. Considering this, a mixed filter pruning approach based on principal component analysis (PCA) and geometric median is presented.

Brain MR Image Enhancement for Tumor Segmentation Using 3D U-Net.

MRI images are visually inspected by domain experts for the analysis and quantification of the tumorous tissues. Due to the large volumetric data, manual reporting on the images is subjective, cumbersome, and error prone. To address these problems, automatic image analysis tools are employed for tumor segmentation and other subsequent statistical analysis.

Multiple Sclerosis Lesion Segmentation in Brain MRI Using Inception Modules Embedded in a Convolutional Neural Network.

Multiple sclerosis (MS) is a chronic and autoimmune disease that forms lesions in the central nervous system. Quantitative analysis of these lesions has proved to be very useful in clinical trials for therapies and assessing disease prognosis. However, the efficacy of these quantitative analyses greatly depends on how accurately the MS lesions have been identified and segmented in brain MRI.

Effects of one-year storage in water on bond strength of self-etching adhesives to enamel and dentin.

The aim of this study was to compare the bond strengths of three self-etching materials during one year of storage. Clearfil SE Bond (SE), Clearfil Protect Bond (PB), and Clearfil Tri-S Bond (TS) were used for bonding to dentin and enamel according to manufacturer's instructions. Microshear bond strength values were measured after 24 hours, six months, and one year.