AIMC Topic: Deep Learning

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Land Use Land Cover Labeling of GLOBE Images Using a Deep Learning Fusion Model.

Sensors (Basel, Switzerland)
Most of the land use land cover classification methods presented in the literature have been conducted using satellite remote sensing images. High-resolution aerial imagery is now being used for land cover classification. The Global Learning and Obse...

Evaluation of Disability Progression in Multiple Sclerosis via Magnetic-Resonance-Based Deep Learning Techniques.

International journal of molecular sciences
Short-term disability progression was predicted from a baseline evaluation in patients with multiple sclerosis (MS) using their three-dimensional T1-weighted (3DT1) magnetic resonance images (MRI). One-hundred-and-eighty-one subjects diagnosed with M...

Transforming L1000 profiles to RNA-seq-like profiles with deep learning.

BMC bioinformatics
The L1000 technology, a cost-effective high-throughput transcriptomics technology, has been applied to profile a collection of human cell lines for their gene expression response to > 30,000 chemical and genetic perturbations. In total, there are cur...

Artificial intelligence (AI) diagnostic tools: utilizing a convolutional neural network (CNN) to assess periodontal bone level radiographically-a retrospective study.

BMC oral health
BACKGROUND: The purpose of this investigation was to develop a computer-assisted detection system based on a deep convolutional neural network (CNN) algorithm and to evaluate the accuracy and usefulness of this system for the detection of alveolar bo...

An investigation of privacy preservation in deep learning-based eye-tracking.

Biomedical engineering online
BACKGROUND: The expanding usage of complex machine learning methods such as deep learning has led to an explosion in human activity recognition, particularly applied to health. However, complex models which handle private and sometimes protected data...

Performance comparison of three deep learning models for impacted mesiodens detection on periapical radiographs.

Scientific reports
This study aimed to develop deep learning models that automatically detect impacted mesiodens on periapical radiographs of primary and mixed dentition using the YOLOv3, RetinaNet, and EfficientDet-D3 algorithms and to compare their performance. Peria...

Deep learning-based quantitative estimation of lymphedema-induced fibrosis using three-dimensional computed tomography images.

Scientific reports
In lymphedema, proinflammatory cytokine-mediated progressive cascades always occur, leading to macroscopic fibrosis. However, no methods are practically available for measuring lymphedema-induced fibrosis before its deterioration. Technically, CT can...

Deep learning for screening primary osteopenia and osteoporosis using spine radiographs and patient clinical covariates in a Chinese population.

Frontiers in endocrinology
PURPOSE: Many high-risk osteopenia and osteoporosis patients remain undiagnosed. We proposed to construct a convolutional neural network model for screening primary osteopenia and osteoporosis based on the lumbar radiographs, and to compare the diagn...

Analysis of Smart Lung Tumour Detector and Stage Classifier Using Deep Learning Techniques with Internet of Things.

Computational intelligence and neuroscience
The use of artificial intelligence (AI) and the Internet of Things (IoT), which is a developing technology in medical applications that assists physicians in making more informed decisions regarding patients' courses of treatment, has become increasi...

Key Information Extraction of Food Environmental Safety Criminal Judgment Documents Based on Deep Learning.

Journal of environmental and public health
Food has an impact on everyone's daily life, the long-term stability of the nation, human survival and development, people's lives and health, and the steady advancement of society. A food safety criminal judgment is a legal document used to record t...