AIMC Topic: Neural Networks, Computer

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A novel adaptive CNN-LSTM fusion network for electrocardiogram diagnosis.

Physiological measurement
Cardiovascular disease (CVD) causes severe global health threat, and electrocardiogram (ECG) is crucial for early CVD diagnosis. Recently, two popular deep learning methods, that is, convolutional neural network (CNN) and long short-term memory (LSTM...

Robust CNN multi-nested-LSTM framework with compound loss for patch-based multi-push ultrasound shear wave imaging and segmentation.

Physics in medicine and biology
Ultrasound shear wave imaging enables noninvasive, quantitative assessment of tissue pathology with mechanical elasticity measurements. However, shear wave elastography (SWE) reconstructions are challenged by noise sensitivity, inefficient multi-push...

Machine learning-based prediction of the axial load capacity of UHPC strengthened reinforced concrete columns: A comparative analysis.

PloS one
This study develops and evaluates machine learning (ML) models to predict the axial load capacity (Pu) of reinforced concrete (RC) columns strengthened with ultra-high-performance concrete (UHPC) jackets. A comprehensive experimental database contain...

Automated Classification of Lymphoma Subtypes From Histopathological Images Using a U-Net Deep Learning Model: Comparative Evaluation Study.

JMIR medical informatics
BACKGROUND: Accurate classification and grading of lymphoma subtypes are essential for treatment planning. Traditional diagnostic methods face challenges of subjectivity and inefficiency, highlighting the need for automated solutions based on deep le...

Morphological and textural descriptors analysis of digital mammograms with radiological findings to support breast cancer detection using artificial neural networks.

Biomedical physics & engineering express
. To classify digital mammograms based on radiological findings using morphology and texture descriptors with artificial neural networks (ANN) for breast cancer detection.The mammography dataset from High Specialty Regional Hospital of Oaxaca (HRAEO)...

Automated retinal disease classification using deep learning and AlexNet with statistical models analysis.

PloS one
Diabetic Retinopathy, Cataract, and Glaucoma are major retinal diseases that require early detection to prevent irreversible vision loss. This study proposes a deep learning-based framework for the automated classification of retinal images into four...

PGMNet: a polyp segmentation network based on bit-plane slicing and multi-scale adaptive fusion.

Biomedical physics & engineering express
Accurate detection and segmentation of polyps during colonoscopy are of great significance for the early prevention and treatment of colorectal cancer. However, due to the considerable variations in polyp size and shape, as well as their blurred boun...

Characterizing DPPM inhibition of butyrylcholinesterase: integrated enzymatic kinetics and Raman spectroscopy with chemometric analysis.

The Analyst
Butyrylcholinesterase (BChE) may serve as a scavenger enzyme protecting against various toxic compounds, but we still don't fully understand how it interacts with fentanyl analogues. We investigated how Despropionyl -methyl fentanyl (DPPM) inhibits e...

Boosting living spore identification: Kolmogorov-Arnold network-guided convolutional neural network combined with laser tweezers Raman spectroscopy.

The Analyst
As primary carriers of foodborne and zoonotic diseases, spores can pose a serious threat to food microbiology and human disease. Thus, the precise identification of spores is of great significance for ensuring food safety and human health. Herein, ...

Contactless biometric verification from in-air signatures using deep siamese networks.

Scientific reports
In-air signature is a behavioral biometric trait that has gained increasing attention in recent years due to its contactless nature and potential for secure, hygienic, and remote authentication. Unlike traditional pen-and-paper or tablet-based system...