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AI-driven convolutional neural networks for accurate identification of yellow fever vectors
BackgroundIdentifying mosquito vectors is crucial for controlling diseases. Automated identification studies using the convolutional neural network...
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An intelligent magnetic resonance imagining-based multistage Alzheimer’s disease classification using swish-convolutional neural networks
Alzheimer’s disease (AD) refers to a neurological disorder that causes damage to brain cells and results in decreasing cognitive abilities and...
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Classification of breast lesions in ultrasound images using deep convolutional neural networks: transfer learning versus automatic architecture design
Deep convolutional neural networks (DCNNs) have demonstrated promising performance in classifying breast lesions in 2D ultrasound (US) images....
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Prediction of leukemia peptides using convolutional neural network and protein compositions
Leukemia is a type of blood cell cancer that is in the bone marrow’s blood-forming cells. Two types of Leukemia are acute and chronic; acute enhances...
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Heart function grading evaluation based on heart sounds and convolutional neural networks
Accurate and rapid cardiac function assessment is critical for disease diagnosis and treatment strategy. However, the current cardiac function...
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Assessment of impaired consciousness using EEG-based connectivity features and convolutional neural networks
Growing electroencephalogram (EEG) studies have linked the abnormities of functional brain networks with disorders of consciousness (DOC). However,...
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A Method to Extract Task-Related EEG Feature Based on Lightweight Convolutional Neural Network
Unlocking task-related EEG spectra is crucial for neuroscience. Traditional convolutional neural networks (CNNs) effectively extract these features...
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Scalogram-Based Gait Abnormalities Classification Using Deep Convolutional Networks for Neurological and Non-Neurological Disorders
PurposeIn the present day, there is a steep increase in cases of neurological and non-neurological diseases that may affect a person’s normal gait....
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EEG-based schizophrenia detection using fusion of effective connectivity maps and convolutional neural networks with transfer learning
Schizophrenia (SZ) is a serious mental disorder that can mainly be distinguished by symptoms including delusions and hallucinations. This mental...
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1D Convolutional Neural Network Impact on Heart Rate Metrics for ECG and BCG Signals
PurposeThe presence of motion artifacts (MA) in cardiac signals negatively impacts the reliability of higher-level information such as the Heart Rate...
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Enhanced premature ventricular contraction pulse detection and classification using deep convolutional neural network
Access to accurate and precise monitoring systems for cardiac arrhythmia could contribute significantly to preventing damage and subsequent heart...
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Convolutional Neural Networks Guided Raman Spectroscopy as a Process Analytical Technology (PAT) Tool for Monitoring and Simultaneous Prediction of Monoclonal Antibody Charge Variants
BackgroundCharge related heterogeneities of monoclonal antibody (mAb) based therapeutic products are increasingly being considered as a critical...
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SST-CRAM: spatial-spectral-temporal based convolutional recurrent neural network with lightweight attention mechanism for EEG emotion recognition
Through emotion recognition with EEG signals, brain responses can be analyzed to monitor and identify individual emotional states. The success of...
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Convolutional neural network-based magnetic resonance image differentiation of filum terminale ependymomas from schwannomas
PurposePreoperative diagnosis of filum terminale ependymomas (FTEs) versus schwannomas is difficult but essential for surgical planning and...
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Performance of Convolutional Neural Network Models in Meningioma Segmentation in Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis
BackgroundMeningioma, the most common primary brain tumor, presents significant challenges in MRI-based diagnosis and treatment planning due to its...
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Prediction of response to repetitive transcranial magnetic stimulation for major depressive disorder using hybrid Convolutional recurrent neural networks and raw Electroencephalogram Signal
Major Depressive Disorder (MDD) is a high prevalence disease that needs an effective and timely treatment to prevent its progress and additional...
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Preliminary Results: Comparison of Convolutional Neural Network Architectures as an Auxiliary Clinical Tool Applied to Screening Mammography in Mexican Women
PurposeMammography is the modality of choice for the early detection of breast cancer. Deep learning, using convolutional neural networks (CNNs)...
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A discriminative shape-texture convolutional neural network for early diagnosis of knee osteoarthritis from X-ray images
Knee Osteoarthritis (OA) is one of the most common causes of physical disability worldwide associated with a significant personal and socioeconomic...
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Predicting the HER2 status in oesophageal cancer from tissue microarrays using convolutional neural networks
BackgroundFast and accurate diagnostics are key for personalised medicine. Particularly in cancer, precise diagnosis is a prerequisite for targeted...
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Pruning and quantization algorithm with applications in memristor-based convolutional neural network
The human brain’s ultra-low power consumption and highly parallel computational capabilities can be accomplished by memristor-based convolutional...
