“…The contour of lungs and lesion locations may be retrieved from CT scans using a CT-image-based quantitative discrimination and trend analysis method for COVID-19 and the Mask R-CNN deep neural network model to create 3D data of lung lesions. …”
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“…We developed 2 convoluted neural networks (CNN) using PyTorch with a Densenet-169 backbone pre-trained on ImageNet and fine-tuned on our data for classifying adequacy of bowel preparation (binary) and for subclassification of BBPS (multi-class). …”
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“…We then developed a convolutional neural network (CNN) to model the imagery change of the nodules from baseline CT image (combined with the nodule mask) to follow-up CT image with a particular time interval. …”
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“…In recent years there have been various algorithms that use artificial intelligence (AI) and neural convolutions (CNN, Convolutional Neural Network) to process images from capsule endoscopy, a non-invasive endoscopy approach, that provides magnified, high qualitative images of the small bowel mucosa, to quickly establish a diagnosis. …”
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“…Test 5 aimed to further compare our results, especially with CNN (Convolutional Neural Networks) methods, by challenging Brain Tumor Segmentation (BraTS) and clinic patient datasets. …”
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“…RESULTS: The logistic regression (LR) classifier produced the overall best performance, outperforming the support vector machine (SVM), random forest (RF), and convolutional neural network (CNN). Among the 30 patients with SUDEP [14 females; mean age (SD), 31 (8.47) years] and 58 living epilepsy controls [26 females (43%); mean age (SD) 31 (8.5) years], the LR model achieved the median AUC of 0.77 [interquartile range (IQR), 0.73–0.80] in five-fold cross-validation using interictal alpha and low gamma power ratio of the EEG and heart rate variability (HRV) features extracted from the ECG. …”
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“…For comparison, an end-to-end CNN model (3D-deep learning model) for brain atrophy classification was also developed. …”
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“…Finally, to implement PHRI into a computer-aided diagnosis system, we trained and tested a novel deep learning strategy based on a CNN architecture to detect neutrophils, calculate PHRI and identify active from quiescent UC using a subset of 138 biopsies. …”
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“…The outcome of this study is a model for augmenting CNN models with ROI-centric image samples for the characterization of abnormalities in breast images.…”
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“…This, in turn, limits the usage of 2-dimensional (2-D) layers adopted in convolutional neural networks (CNN) for embedding spatial information in the model. …”
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“…Moreover, for the detection of COVID-19, we also proposed a novel technique, employing the two features, namely, Histogram of Oriented Gradients and Scale Invariant Feature Transform. We also designed a CNN-based architecture named COVIDDetectorNet for classification purposes. …”
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“…A deep learning approach based on a convolutional neural network (CNN) is performed for feature extraction. Four different pre-trained models are applied, such as Vgg16, Vgg19, ResNet-101, and Xception. …”
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“…Each network branch consists of a convolutional neural network (CNN) unit and a spatial transform unit. To evaluate the registration performance on a dataset that has no ground truth, we propose an evaluation strategy that is based on comparing automatic cervix segmentation masks in the registered sequence and the original sequence. …”
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“…METHODS: As deep learning approaches have gained significant attention in recent years due to their remarkable performance, therefore, in this work, we proposed a novel design of a convolutional neural network (CNN) framework based on atrous convolutions for automatic lesion segmentation. …”
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“…In method 1, ResNet50 convolutional neural network (CNN) model pre-trained on the ImageNet dataset was used for extracting feature maps from spectrogram images (ImageNet-ResNet) of iEEG records. …”
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“…Hand-crafted features and deep radiomics features were extracted respectively by dedicated software (MODDICOM) and a dedicated convolutional neural network (CNN). Features were selected with and without prior harmonization (ComBat harmonization), and models were built using different machine learning algorithms, including clinical variables. …”
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“…Then, based on the ultrasound signs of breast cancer lesions, we screened ten ML models: support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), linear discriminant analysis (LDA), logistic regression (LR), naive bayesian model (NB), k-nearest neighbors (KNN), multilayer perceptron (MLP), long short-term memory (LSTM), and convolutional neural network (CNN). The diagnostic performance of the model was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), Kappa value, accuracy, F1-score, sensitivity, and specificity. …”
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“…Then, text convolutional neural network (TextCNN) was used to build models for disease prediction and examination recommendation. …”
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“…There were two technology frontiers in the budding period (1992–2000), namely systems and methods for image data processing in computerized tomography (CT), and immunohistochemistry (IHC), five technology frontiers in the development period (2001–2015), namely spectral analysis methods of biomacromolecules, pathological information system, diagnostic biomarkers, molecular pathology diagnosis, and pathological diagnosis antibody, and six technology frontiers in the rapid growth period (2016–2021), namely digital pathology (DP), deep learning (DL) algorithms—convolutional neural networks (CNN), disease prediction models, computational pathology, pathological image analysis method, and intelligent pathological system. …”
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“…The combined sensitivity, specificity, and AUC of the AI-aided EUS diagnosis in the convolutional neural network (CNN) model were 0.93, 0.81, and 0.94, respectively. …”
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