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  1. 8601
    “…FINDINGS: We used this dataset to test whether a CNN-based AI algorithm could contribute to leprosy diagnosis and employed three AI models, testing images and metadata both independently and in combination. …”
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    Online Artículo Texto
    Reimagining leprosy elimination with AI analysis of a combination of skin lesion images with demographic and clinical data
  2. 8602
    “…Accurate and prompt diagnosis is essential in LLD; hence, this study aimed to combine CNN and CSE analysis to discriminate LLD patients and non-depressed comparison older adults based on brain resting-state fMRI signals. …”
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    Automatic diagnosis of late-life depression by 3D convolutional neural networks and cross-sample Entropy analysis from resting-state fMRI
  3. 8603
    “…A deep learning algorithm utilizing a convolutional neural network (CNN) was trained to classify T2W FLAIR images according to Engel’s classification. …”
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    Online Artículo Texto
    Developing a deep learning model to predict epilepsy recurrence in patients with focal cortical dysplasia type III
  4. 8604
    “…We extracted features using a deep convolutional neural network (CNN) and built a hybrid model to combine periapical and panoramic images. …”
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    Online Artículo Texto
    Deep learning based dental implant failure prediction from periapical and panoramic films
  5. 8605
    “…Third, a multi-task deep convolutional neural network (CNN) was customized to take a raw imagery data fusion of hyperspectral, thermal, and LiDAR for multi-predictions of maize traits at a time. …”
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    Online Artículo Texto
    UAV Multisensory Data Fusion and Multi-Task Deep Learning for High-Throughput Maize Phenotyping
  6. 8606
    “…To substantiate the effectiveness of feature engineering besides semantic features, we proposed a deep neural architecture in which three parallel convolutional neural network (CNN) layers extract semantic features from contextual representation vectors. …”
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    Online Artículo Texto
    Fake news detection: deep semantic representation with enhanced feature engineering
  7. 8607
    por Voigt, Johann Christoph
    Publicado 2023
    “…Five NN algorithms based on CNN, RNN, and LSTM architectures will be presented. …”
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    Machine Learning for Real-Time Processing of ATLAS Liquid Argon Calorimeter Signals with FPGAs
  8. 8608
    por Chiedde, Nemer
    Publicado 2023
    “…Five NN algorithms based on CNN, RNN, and LSTM architectures will be presented. …”
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    Machine Learning for Real-Time Processing of ATLAS Liquid Argon Calorimeter Signals with FPGAs
  9. 8609
    “…After augmentation with pseudo-color image fusion, MRI images were fed into the developed cascade feature pyramid network system, feature pyramid network, and faster region-based convolutional neural network (CNN) for breast lesion detection and classification, respectively. …”
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    Online Artículo Texto
    Automatic deep learning method for detection and classification of breast lesions in dynamic contrast-enhanced magnetic resonance imaging
  10. 8610
    “…The knowledge transfer capability of an established model architecture for phase recognition (CNN + LSTM) was adapted to generate a “Transferal Esophagectomy Network” (TEsoNet) for co-training and transfer learning from laparoscopic Sleeve Gastrectomy to the laparoscopic part of Ivor-Lewis Esophagectomy, exploring different training set compositions and training weights. …”
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    Online Artículo Texto
    TEsoNet: knowledge transfer in surgical phase recognition from laparoscopic sleeve gastrectomy to the laparoscopic part of Ivor–Lewis esophagectomy
  11. 8611
    “…The simplified process is as follows: (1) Raw EEG data acquisition and preprocessing. (2) The time series EEG data of each channel are input as recurrent neural network (RNN), and RNN is used to process and extract temporal domain (TD) features. (3) The BFN among different EEG channels is constructed, and CNN is used to process and extract the spatial domain (SD) features of the BFN. (4) Based on the theory of information complementarity, the spatial–temporal information is fused to realize efficient MDD detection. …”
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    Spatial–Temporal EEG Fusion Based on Neural Network for Major Depressive Disorder Detection
  12. 8612
    “…Methods: A convolutional neural network (CNN) was implemented, taking cine MRI as the input and the in-plane velocity derived from the 4D flow acquisition as the ground truth. …”
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    Deep learning-based prediction of intra-cardiac blood flow in long-axis cine magnetic resonance imaging
  13. 8613
    “…Most imaging works explored convolutional neural networks (CNN), such as VGG and Resnet. Then transfer learning which stands out among the techniques related to deep learning has the second highest frequency of use. …”
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    Machine Learning Applied to COVID-19: A Review of the Initial Pandemic Period
  14. 8614
    “…Existing models for circRNA-RBP identification usually adopt convolution neural network (CNN), recurrent neural network (RNN), or their variants as feature extractors. …”
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    CircSSNN: circRNA-binding site prediction via sequence self-attention neural networks with pre-normalization
  15. 8615
    “…METHODS: To address the need to accurately determine the presence and extent of plaque components on carotid plaque MRI, we proposed a two-staged deep-learning-based approach that consists of a convolutional neural network (CNN), followed by a Bayesian neural network (BNN). …”
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    Carotid atherosclerotic plaque segmentation in multi-weighted MRI using a two-stage neural network: advantages of training with high-resolution imaging and histology
  16. 8616
    por Xu, Huangchao, Zhang, Baohua, Liu, Qian
    Publicado 2023
    “…The results demonstrate high classification accuracy and stability, with the optimal model Mol2vec-CNN significantly improving performance across multiple classifiers. …”
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    Deep learning-based classification model for GPR151 activator activity prediction
  17. 8617
    “…The CXRs were bijectively projected into the 2D domain by performing Uniform Manifold Approximation and Projection (UMAP) embedding on the radiomic features (rUMAP) or CNN-based neural features (nUMAP) from the pre-last layer of the pre-trained classification neural network. …”
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    Online Artículo Texto
    Pathological changes or technical artefacts? The problem of the heterogenous databases in COVID-19 CXR image analysis
  18. 8618
    “…The recent deep convolutional neural network (CNN) RegNetX032 was adapted for detecting COVID-19 from chest X-ray (CXR) images using polymerase chain reaction (RT-PCR) as a reference. …”
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    Explainable COVID-19 Detection Based on Chest X-rays Using an End-to-End RegNet Architecture
  19. 8619
    “…A convolutional neural network (CNN) model was trained to assess the developmental states that appear in single frames from 20,253 manually-annotated embryos. …”
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    Online Artículo Texto
    Delineating the heterogeneity of embryo preimplantation development using automated and accurate morphokinetic annotation
  20. 8620
    “…Among all ML techniques, methods based on convolutional neural networks (CNN) achieved higher accuracy and sensitivity in the early detection of EC compared to other methods. …”
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    Online Artículo Texto
    Machine learning applications for early detection of esophageal cancer: a systematic review
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