“…Three different machine learning approaches are applied: a random forest (RF), a fully connected neural network (NN) and a 1D convolutional neural network (CNN). Additionally, different preprocessing steps are investigated. …”
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“…Experimental results reveal that ResNet-50 model on the augmented dataset (by SVD-CLAHE Boosting), along with BWCCE loss function, achieved 95% F1 score, 94% accuracy, 95% recall, 96% precision and 96% AUC, which is far better than the results by other conventional Convolutional Neural Network (CNN) models like InceptionV3, DenseNet-121, Xception etc. as well as other existing models like Covid-Lite and Covid-Net. …”
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“…A deep learning model using 3D convolutional neural network (CNN) and multilayer perceptron is trained based on CT scans and minimum extra information (MEI) including text input of patient age and gender as well as automatically extracted lesion location and size from image data. …”
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“…These processed images are then provided as input to a VGG inspired deep Convolutional Neural Network (CNN) model which takes one channel image data as input (grayscale images) to categorize CXRs into three class labels, namely, No-Findings, COVID-19, and Pneumonia. …”
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“…In this work, three feature fusion strategies have been proposed by utilizing three pre-trained Convolutional Neural Network (CNN) models, namely VGG16, EfficientNet B0, and ResNet50 to select the important features based on the weights of the features and are coined as Adaptive Weighted Feature Set (AWFS). …”
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“…An advanced convolutional neural network (CNN) was used to generate classifier models to detect ALK and ROS1-fusions directly from scanned hematoxylin and eosin (H&E) whole slide images prepared from NSCLC tumors of patients. …”
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“…The first FCNN is trained to be more sensitive revealing possible candidate new lesion voxels, while the second FCNN is trained to reduce the number of misclassified voxels coming from the first network. 3D T2-FLAIR images from the two-time points were pre-processed and linearly co-registered. Afterward, a fully CNN, where its inputs were only the baseline and follow-up images, was trained to detect new MS lesions. …”
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“…The stage I model was innovatively employed for stage II training to improve accuracy with the idea of transfer learning (TL). The Mask R-CNN instance segmentation framework was selected for model construction and training. …”
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“…The robustness of our model is achieved by combining: Data Missing Care (DMC) Framework to overcome the problem of high missing data in medical datasets, and Grid-Search optimization used to develop an improved deep predictive training model for patients with COVID-19 by setting multiple hyperparameters and tuning assessments on three deep learning algorithms: ANN (Artificial Neural Network), CNN (Convolutional Neural Network), and Recurrent Neural Networks (RNN). …”
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“…After a balanced data set was prepared, feature vectors were obtained from images using deep learning-based CNN models and the size of feature vectors was reduced by feature selection algorithms. …”
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“…In this study, a deep-learning-based framework for IA identification and segmentation was developed, and the impacts of image pre-processing and convolutional neural network (CNN) architectures on the framework’s performance were investigated. …”
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“…To address this challenge, we present a workflow for real-time detection of “Signal for Help” based on two lightweight CNN architectures, dedicated to hand palm detection and hand gesture classification, respectively. …”
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“…A total of four hub genes (BCL6, CCL5, CNN1, and PCNT) were validated using RF, LASSO, Boruta, and SVM-RFE algorithms. …”
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“…RESULTS: A modified skip connection-based CNN model was suggested for a small unbalanced dataset (Kaggle) and achieved remarkable performance. …”
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“…In this paper, we propose our own model, which is called iU-Net bacause its structure closely resembles the combination of i and U. iU-Net is a multiple encoder-decoder structure combining Swin Transformer and CNN. We use a hierarchical Swin Transformer structure with shifted windows as the primary encoder and convolution as the secondary encoder to complement the context information extracted by the primary encoder. …”
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“…Such a high accuracy allowed the CNN ensemble to uncover new insights over our previously published studies. …”
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“…The categorized images were then processed in a training, validation, and test run by the ImageNet pretrained CNN frameworks (Inception ResNet v2, Inception v3, ResNet152, Xception) in different pixel sizes. …”
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“…With the development of deep learning, convolutional neural networks (CNN) have been extensively used in image classification and object detection. …”
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“…Multivariate analyses confirmed that cervical rCAI was an independent prognosticator for DFS and DFFS, surpassing other nodal features, such as laterality, size, cervical node necrosis (CNN) and radiologic extranodal extension (rENE), while location of positive LNs remained independently associated with OS, DFS and DFFS. …”
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“…In this study, various machine learning algorithms were used (random forest, RF; convolutional neural networks, CNN; extreme gradient boosting, XGBoost; ElasticNetCV; Bayesian Ridge; and particle swarm optimization-support vector regression) to select the most suitable algorithm for predicting and comparing the quality of potential source rocks. …”
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