“…We design a backbone network dominated by ResNeXt50 and supplemented by dilated convolution to increase the network depth, expand the perceptual field, and improve the efficiency of feature extraction without increasing the parameters. …”
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“…All four types also showed significantly higher deep learning (DL) performance than ResNeXt101 alone. For image classification of liver masses using US images, ensemble learning improved the performance of DL over a single CNN.…”
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“…We compare Mask RCNN, Cascade RCNN, and Hybrid Task Cascade (HTC) methods, while testing RsNeXt 101, Swin-S and HRNetV2p backbones, with transfer learning for localization and instance segmentation. …”
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“…To address this problem, this paper improved the Mask R-CNN by replacing the backbone network ResNet with the ResNeXt network with group convolution to further improve the feature extraction capability of the model. …”
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“…Our network model achieves better performance than other mainstream networks under the same training parameters, the accuracy we achieved is 3.08 percentage point higher than ResNet50 and 1.38 percentage points higher than ResNeXt50. Compared with SeResNet152, it is 0.29 percentage point higher in the case of 50 epochs less training.…”
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“…Our selection of student models includes the Transformer-based BEiT and the CNN-based ConvNeXt, which achieve accuracies of 98.77% and 96.88%, respectively. …”
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“…The highlights of this issue include, among others, a description of neXtProt, a knowledgebase on human proteins; a detailed explanation of the principles behind the NCBI Taxonomy Database; NCBI and EBI papers on the recently launched BioSample databases that store sample information for a variety of database resources; descriptions of the recent developments in the Gene Ontology and UniProt Gene Ontology Annotation projects; updates on Pfam, SMART and InterPro domain databases; update papers on KEGG and TAIR, two universally acclaimed databases that face an uncertain future; and a separate section with 10 wiki-based databases, introduced in an accompanying editorial. …”
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“…All these data are available to the scientific community via neXtProt at https://www.nextprot.org/portals/navmut.…”
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“…Expert diagnoses served as the reference standard for cyclic training and repeated evaluation of the CNN (ResNeXt-101-32x8d), which was trained by using image augmentation and transfer learning. …”
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“…The proposed approach consists of a deep neural pipeline for corneal region segmentation followed by a ResNeXt model to differentiate between FK and non-FK classes. …”
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“…By utilizing a comparativelyightweight backbone of ResNet-50, this paper demonstrates that superior results are attainable without relying on pre- and post-processing methods, heavier backbone networks (ResNet-101, ResNeXt-152), and memory-intensive deformable convolutions. …”
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“…First, the encoding-decoding structure is used to construct a pavement crack segmentation network, ResNeXt50 is used to extract features in the encoding stage, and a multiscale feature fusion module (MFF) is designed to obtain multiscale context information. …”
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“…In the post hoc data analysis, we found a dubious or uncertain protein (PSG7) encoded on human chromosome 19 according to neXtProt. A proteomic sample preparation workflow with the 1DE-gel concentration can be used as a prospective tool for uncovering the low-abundance part of the human proteome.…”
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“…Eight DL algorithms, including ResNet50, DenseNet121, ResNeXt50, SE-ResNet50, and EfficientNets B0 to B3, were adopted as backbone models to train and obtain the best ensemble 2-, 3-, 4-, and 5-DL models. …”
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“…We propose a novel end-to-end retinal layer segmentation network based on ConvNeXt, which can retain more feature map details by using a new depth-efficient attention module and multi-scale structures. …”
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“…Additionally, 40 records on CHST genetic and biochemical properties were hand-picked from UniProt, GeneCards, InterPro, and neXtProt databases. Carbohydrate sulfotransferases have been applied mainly in diagnostics of connective tissue disorders, cancer and inflammations. …”
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“…This study further modified the architectures: AlexNet, VGG16, VGG19, ResNet50, ResNeXt50, MobileNet, and MobileNetV2 to utilize multimodal data cubes composed of RGB and hyperspectral data for sensitivity analyses. …”
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“…In the feature extraction part, ResNeXt is introduced to learn unitary feature extraction, and the ASPP (Atrous Spatial Pyramid Pooling) module is trained to extract multiscale spatial feature information. …”
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“…Second, in order to improve the adaptability and accuracy of the detection algorithm, we constructed a holistic detection model DR-IIXRN, which consists of Inception V3, InceptionResNet V2, Xception, ResNeXt101, and NASNetLarge. For each base classifier, we modified the network model using transfer learning, fine-tuning, and attention mechanisms to improve its ability to detect DR. …”
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“…The proposed hybrid Faster R-CNN with SE-ResNeXt-101 transfer learning model performed better in classifying echocardiogram images, with 98.06% precision, 98.95% recall, 96.32% specificity, a 99.02% F-score, and maximum accuracy of 99.15%.…”
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