Convolutional Neural Networks-Based Framework for Early Identification of Dementia Using MRI of Brain Asymmetry.

Computer-aided diagnosis of health problems and pathological conditions has become a substantial part of medical, biomedical, and computer science research. This paper focuses on the diagnosis of early and progressive dementia, building on the potential of deep learning (DL) models. The proposed computational framework exploits a magnetic resonance imaging (MRI) brain asymmetry biomarker, which has been associated with early dementia, and employs DL architectures for MRI image classification.

Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia.

Early identification of degenerative processes in the human brain is considered essential for providing proper care and treatment. This may involve detecting structural and functional cerebral changes such as changes in the degree of asymmetry between the left and right hemispheres. Changes can be detected by computational algorithms and used for the early diagnosis of dementia and its stages (amnestic early mild cognitive impairment (EMCI), Alzheimer's Disease (AD)), and can help to monitor the progress of the disease.

Evolving Connectionist Models to Capture Population Variability across Language Development: Modeling Children's Past Tense Formation.

Children's acquisition of the English past tense has been widely studied as a testing ground for theories of language development, mostly because it comprises a set of quasi-regular mappings. English verbs are of two types: regular verbs, which form their past tense based on a productive rule, and irregular verbs, which form their past tenses through exceptions to that rule. Although many connectionist models exist for capturing language development, few consider individual differences.

Neuronal networks and textural descriptors for automated tissue classification in endoscopy.

This study examines the potential of neuronal networks and textural feature extraction for recognising suspicious regions in endoscopy under variable perceptual conditions and systematic or random noise in the data. Second-order statistics and discrete wavelet transform-based methodologies are examined in terms of their discrimination abilities, and several neuronal network learning algorithms are compared in terms of success. The results provide numerical evidence that neuronal networks are capable of classifying offline and online tissue samples extracted from standard images and VHS videotape recordings of colonoscopy procedures with satisfactory success rates.

Improved processing of microarray data using image reconstruction techniques.

Spotted cDNA microarray data analysis suffers from various problems such as noise from a variety of sources, missing data, inconsistency, and, of course, the presence of outliers. This paper introduces a new method that dramatically reduces the noise when processing the original image data. The proposed approach recreates the microarray slide image, as it would have been with all the genes removed.

Effective Backpropagation Training with Variable Stepsize.

The issue of variable stepsize in the backpropagation training algorithm has been widely investigated and several techniques employing heuristic factors have been suggested to improve training time and reduce convergence to local minima. In this contribution, backpropagation training is based on a modified steepest descent method which allows variable stepsize. It is computationally efficient and posseses interesting convergence properties utilizing estimates of the Lipschitz constant without any additional computational cost.