“…To be specific, we first employ ConvNeXt as the backbone of the segmentation and reconstruction network to enhance the feature extraction capability of the encoder. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…Results: The Swin-Transformer, ConvNeXt, and GoogLeNet training models had optimal results, with an accuracy of approximately 0.94; VGG11, VGG16, VGG19, ResNet 34, and ResNet 50, which are neural network models with fewer layers, achieved relatively strong results; and Transformer and other neural networks with more layers or neural network models with larger receptive fields exhibited a relatively weak performance. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…The model is based on a state-of-the-art ConvNeXt CNN architecture with weights fine-tuned for the given specific application using the cornea scans dataset. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…To address this problem, we propose a novel multi-supervised bidirectional fusion network (MBFN) model to detect weather-induced road-surface conditions on the path of automatic vehicles at both daytime and nighttime. We employed ConvNeXt to extract the basic features, which were further processed using a new bidirectional fusion module to create a fused feature. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…The dataset used in this study contains 616 missing proteins from the “uncertain” category of the neXtProt database. There are 102 proteins deduced by the n-gram model, which have high probability to be native human proteins. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…Here, we developed a novel CNN model named YOLO-S-CIOU, which was improved based on YOLOv3 for specific building detection in two aspects: (1) module Darknet53 in YOLOv3 was replaced with SRXnet (constructed by superimposing multiple SE-ResNeXt) to significantly improve the feature learning ability of YOLO-S-CIOU while maintaining the similar complexity as YOLOv3; (2) Complete-IoU Loss (CIoU Loss) was used to obtain a better regression for the bounding box. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…Toward simplicity and a highly modularized network for image classification, we leverage the ResNeXt-50 block for our model. Furthermore, improving the separability among classes and balance of the interclass and intraclass criteria, we engaged principal component analysis (PCA) for the best orthogonal vectors for representing information from HSIs before feeding to the network. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…The experiments show that the decoder-based Unet family has reached the best (a mean Intersection Over Union (mIoU) of 0.9234, 0.9032 in dice score, and a recall of 0.9349) with a combination between SE ResNeXt and Unet++. The decoder with the Unet family obtained better COVID segmentation performance in comparison with Feature Pyramid Network. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…The experimental results show that the accuracy of the CA-ENet is 98.92% on the ALDID, and the average F1-score reaches .988, which is better than those of common models such as the ResNet-152, DenseNet-264, and ResNeXt-101. The generated test dataset is used to test the anti-interference ability of the model. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…Building on the ideas from those works, we propose a novel bilateral knowledge distillation regime that enables multiple interactions between teacher and student models, i.e., teaching and distilling each other, eventually improving each other's performance and evaluating our results on BACH histopathology image dataset on breast cancer. The pretrained ResNeXt29 and MobileNetV2 models which are already tested on ImageNet dataset are used for “transfer learning” in our dataset, and we obtain a final accuracy of more than 96% using this novel approach of bilateral KD.…”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…A Long Short-Term Memory (LSTM) recurrent neural network was applied for glomerular disease classification, and another two-stage model using ResNeXt-101 was constructed for glomerular lesion identification in cases of lupus nephritis. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…The highest single network score was 0.9586 using the SE-ResNeXt architecture. For individual disease classification, the average AUROC score for each class was 0.9295. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…The network is based on YOLOv7 with the insertion of the CSPResNeXt-50 module and VoVGSCSP module. It can improve network detection accuracy and detection speed while reducing the computational effort of the model. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…Firstly, an improved backbone network ConvNeXt-E was proposed to extract sheep features. Secondly, we improved the structure of the two-stage object detector Dynamic R-CNN to precisely locate highly overlapping sheep. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…Our model achieved an RMSE of 3.52 kg on the test set, which is lower than that of the pig body mass estimation algorithm with ResNet and ConvNeXt as the backbone network, and the average estimation speed was 0.339 s·frame(−1) Our model can evaluate the body quality of pigs in real-time to provide data support for grading and adjusting breeding plans, and has broad application prospects.…”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…An extensive privacy benchmark is conducted on three different state-of-the-art model architectures (ResNet50, NFNet, ConvNeXt) trained on two biomedical (ISIC and EyePACS) and one synthetic dataset (SCDB). …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…This consists of (1) a convolutional neural network model integrated with multiple attention mechanisms which simultaneously segments caudate nucleus, globus pallidus, putamen, red nucleus, and substantia nigra from QSM and T1W images, and (2) an SE‐ResNeXt50 model with an anatomical attention mechanism, which uses QSM data and the segmented nuclei to distinguish PD from HC. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…In contrast, the comparison models CSPDenseNet and ConvNeXt were significantly inferior to the proposed model, with 0.819 (p = 0.029) and 0.774 (p = 0.04) AUROC values, respectively. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…Compared with the existing advanced network models VGG-16, ResNet-50, GoogleNet, ViT, AlexNet, MobileViT, ConvNeXt, ShuffleNet, and RepVGG_b0, our model has demonstrated the best performance in a lot of indicators. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…The 4-way cross-reference concordance (within UniProt) between Ensembl, Swiss-Prot, Entrez Gene and the Human Gene Nomenclature Committee (HGNC) drops to 18,690, indicating methodological differences in protein definitions and experimental existence support between sources. The Swiss-Prot and neXtProt evidence criteria include mass spectrometry peptide verification and also cross-references for antibody detection from the Human Protein Atlas. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso