AIMC Topic: Convolutional Neural Networks

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HS-GC-IMS couples with convolutional neural network for Burkholderia gladioli pv. Cocovenenans detection in Auricularia Auricula.

Food chemistry Sep 15, 2025
The shortage in early detection methods for the pathogen Burkholderia gladioli pv. cocovenenans (BGC) and its toxin bongkrekic acid rises the risk for food poisoning. Combining Headspace-Gas Chromatography-Ion Mobility Spectrometry (HS-GC-IMS) with c...
Basidiomycota Food Contamination Burkholderia gladioli Limit of Detection Neural Networks, Computer Gas Chromatography-Mass Spectrometry Convolutional Neural Networks
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Implementation of convolutional neural networks for microbial colony recognition.

Microbiology spectrum Sep 2, 2025
Initial classification of microorganisms based on visual identification of colonies remains challenging for skilled microbiologists and is influenced by the proficiency and subjective interpretation of professionals. To overcome these challenges, we ...
Fungi Bacteria Candida Humans Neural Networks, Computer Deep Learning Convolutional Neural Networks
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Ensemble-based Convolutional Neural Networks for brain tumor classification in MRI: Enhancing accuracy and interpretability using explainable AI.

Computers in biology and medicine Sep 1, 2025
BACKGROUND: Accurate and efficient classification of brain tumors, including gliomas, meningiomas, and pituitary adenomas, is critical for early diagnosis and treatment planning. Magnetic resonance imaging (MRI) is a key diagnostic tool, and deep lea...
Humans Neural Networks, Computer Magnetic Resonance Imaging Image Interpretation, Computer-Assisted Male Deep Learning Convolutional Neural Networks Female Brain Neoplasms
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Temporal convolutional neural network-based feature extraction and asynchronous channel information fusion method for heart abnormality detection in phonocardiograms.

Computer methods and programs in biomedicine Sep 1, 2025
BACKGROUND AND OBJECTIVE: Auscultation-based cardiac abnormality detection is valuable screening approach in pediatric populations, particularly in resource-limited settings. However, its clinical utility is often limited by phonocardiogram (PCG) sig...
ROC Curve Signal Processing, Computer-Assisted Phonocardiography Convolutional Neural Networks Child Humans Neural Networks, Computer Algorithms
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Denoising of high-resolution 3D UTE-MR angiogram data using lightweight and efficient convolutional neural networks.

Magnetic resonance imaging Sep 1, 2025
High-resolution magnetic resonance angiography (∼ 50 μm MRA) data plays a critical role in the accurate diagnosis of various vascular disorders. However, it is very challenging to acquire, and it is susceptible to artifacts and noise which limits its...
Contrast Media Artifacts Signal-To-Noise Ratio Magnetic Resonance Angiography Phantoms, Imaging Convolutional Neural Networks Imaging, Three-Dimensional Image Processing, Computer-Assisted Neural Networks, Computer Algorithms Humans
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Harnessing deep learning for wheat variety classification: a convolutional neural network and transfer learning approach.

Journal of the science of food and agriculture Sep 1, 2025
BACKGROUND: Computer vision and the use of image-based solutions are gaining traction as non-destructive food assessment methods because of the low costs of computational equipment. Research conducted on the development of wheat classification models...
Triticum Deep Learning Convolutional Neural Networks Image Processing, Computer-Assisted
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Pruning the ensemble of convolutional neural networks using second-order cone programming.

Neural networks : the official journal of the International Neural Network Society Sep 1, 2025
Ensemble techniques are frequently encountered in machine learning and engineering problems since the method combines different models and produces an optimal predictive solution. The ensemble concept can be adapted to deep learning models to provide...
Deep Learning Convolutional Neural Networks Neural Networks, Computer Machine Learning Algorithms
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Accelerated ultrashort echo time quantitative magnetization transfer (UTE-qMT) imaging of macromolecular fraction (MMF) in cortical bone based on a self-attention convolutional neural network.

Magnetic resonance imaging Sep 1, 2025
PURPOSE: To combine ultrashort echo time quantitative magnetization transfer (UTE-qMT) imaging with a self-attention convolutional neural network (SAT-Net) for accelerated mapping of macromolecular fraction (MMF) in cortical bone.
Bone Diseases, Metabolic Cortical Bone Humans Adult Middle Aged Convolutional Neural Networks Image Processing, Computer-Assisted Magnetic Resonance Imaging Tibia Neural Networks, Computer Osteoporosis Algorithms Female Young Adult
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Comprehensive quality evaluation of crude material of Ligusticum chuanxiong Hort. through high performance liquid chromatography coupled with DenseNet-121 assisted hyperspectral imaging and anti-thrombotic zebrafish bioassay.

Journal of pharmaceutical and biomedical analysis Sep 1, 2025
An innovative, integrated strategy was developed for rapid and comprehensive quality assessment of Ligusticum chuanxiong Hort., the key raw material for Guanxinning tablets. This approach simultaneously evaluates both chemical composition and biologi...
Thrombosis Zebrafish Chromatography, High Pressure Liquid Least-Squares Analysis Biological Assay Coumaric Acids Ligusticum Animals Quality Control Convolutional Neural Networks Hyperspectral Imaging Drugs, Chinese Herbal Calibration
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The Development and Evaluation of a Convolutional Neural Network for Cutaneous Melanoma Detection in Whole Slide Images.

Archives of pathology & laboratory medicine Sep 1, 2025
CONTEXT.—: The current melanoma staging system does not account for 26% of the variance seen in melanoma-specific survival, therefore our ability to predict patient outcome is not fully elucidated. Morphology may be of greater significance than in ot...
Humans Neural Networks, Computer Convolutional Neural Networks Melanoma, Cutaneous Malignant Image Interpretation, Computer-Assisted Prognosis Melanoma Skin Neoplasms
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