Search Results (4)

Background: Accurate staging of bladder cancer is critical for guiding clinical management, particularly the distinction between non–muscle-invasive (T1) and muscle-invasive (T2–T4) disease. Although MRI offers superior soft-tissue contrast, image interpretation remains opera-tor-dependent and subject to inter-observer variability. This study proposes an automated deep learning framework for MRI-based bladder cancer staging to support standardized radio-logical interpretation. Methods: A sequential AI-based pipeline was developed, integrating hybrid tumor segmentation using YOLOv11 for lesion detection and DeepLabV3 for boundary refinement, followed by three deep learning classifiers (VGG19, ResNet50, and Vision Transformer) for MRI-based stage prediction. A total of 416 T2-weighted MRI images with radiology-derived stage labels (T1–T4) were included, with data augmentation applied during training. Model performance was evaluated using accuracy, precision, recall, F1-score, and multi-class AUC. Performance un-certainty was characterized using patient-level bootstrap confidence intervals under a fixed training and evaluation pipeline. Results: All evaluated models demonstrated high and broadly comparable discriminative performance for MRI-based bladder cancer staging within the present dataset, with high point estimates of accuracy and AUC, particularly for differentiating non–muscle-invasive from muscle-invasive disease. Calibration analysis characterized the probabilistic behavior of predicted stage probabilities under the current experimental setting. Conclusions: The proposed framework demonstrates the feasibility of automated MRI-based bladder cancer staging derived from radiological reference labels and supports the potential of deep learning for stand-ardizing and reproducing MRI-based staging procedures. Rather than serving as an independent clinical decision-support system, the framework is intended as a methodological and work-flow-oriented tool for automated staging consistency. Further validation using multi-center datasets, patient-level data splitting prior to augmentation, pathology-confirmed reference stand-ards, and explainable AI techniques is required to establish generalizability and clinical relevance. Full article

The identification of the yarn roll’s margin represents a critical phase in the automated production of textiles. At present, conventional visual detection techniques are inadequate for accurately measuring, filtering out background noise, and generalizing the margin of the yarn roll. To address this issue, this study constructed a semantic segmentation dataset for the yarn roll and proposed a new method for detecting the margin of the yarn roll based on deep learning. By replacing the encoder component of the U-Net with the initial 13 convolutional layers of VGG16 and incorporating pre-trained weights, we constructed a VGG-UNet model that is well suited for yarn roll segmentation. A comparison of the results obtained on the test set revealed that the model achieved an average Intersection over Union (IoU) of 98.70%. Subsequently, the contour edge point set was obtained through the application of traditional image processing techniques, and contour fitting was performed. Finally, the actual yarn roll margin was calculated based on the relationship between pixel dimensions and actual dimensions. The experiments demonstrate that the margin of the yarn roll can be accurately measured with an error of less than 3 mm. This is particularly important in situations where the margin is narrow, as the detection accuracy remains high. This study provides significant technical support and a theoretical foundation for the automation of the textile industry. Full article

Highly accurate urban vegetation extraction is important to supporting ecological and management planning in urban areas. However, achieving high-precision classification of urban vegetation is challenging due to dramatic land changes in cities, the complexity of land cover, and hill shading. Although convolutional neural networks (CNNs) have unique advantages in remote sensing image classification, they require a large amount of training sample data, making it difficult to adequately train the network to improve classification accuracy. Therefore, this paper proposed an urban vegetation classification method by combining the advantages of transfer learning, deep learning, and ensemble learning. First, three UNet++ networks (UNet++, VGG16-UNet++, and ResNet50-UNet++) were pre-trained using the open sample set of urban land use/land cover (LULC), and the deep features of Sentinel-2 images were extracted using the pre-trained three UNet++ networks. Subsequently, the optimal deep feature set was then selected by Relief-F and input into the Stacking algorithm for urban vegetation classification. The results showed that deeper features extracted by UNet++ networks were able to easily distinguish between different vegetation types compared to Sentinel-2 spectral features. The overall classification accuracy (OA) of UNet++ networks and the Stacking algorithm (UNS) was 92.74%, with a Kappa coefficient of 0.8905. The classification results of UNet++ networks and the Stacking algorithm improved by 2.34%, 1.8%, 2.29%, and 10.74% in OA compared to a single neural network (UNet++, VGG16-UNet++, and ResNet50-UNet++) and the Stacking algorithm, respectively. Furthermore, a comparative analysis of the method with common vegetation classification algorithms (RF, U-Net, and DeepLab V3+) indicated that the results of UNS were 11.31%, 9.38%, and 3.05% better in terms of OA, respectively. Generally, the method developed in this paper could accurately obtain urban vegetation information and provide a reference for research on urban vegetation classification. Full article

We propose a practical Convolution Neural Network (CNN) model termed the CNN for Semantic Segmentation for driver Assistance system (CSSA). It is a novel semantic segmentation model for probabilistic pixel-wise segmentation, which is able to predict pixel-wise class labels of a given input image. Recently, scene understanding has turned out to be one of the emerging areas of research, and pixel-wise semantic segmentation is a key tool for visual scene understanding. Among future intelligent systems, the Advanced Driver Assistance System (ADAS) is one of the most favorite research topic. The CSSA is a road scene understanding CNN that could be a useful constituent of the ADAS toolkit. The proposed CNN network is an encoder-decoder model, which is built on convolutional encoder layers adopted from the Visual Geometry Group’s VGG-16 net, whereas the decoder is inspired by segmentation network (SegNet). The proposed architecture mitigates the limitations of the existing methods based on state-of-the-art encoder-decoder design. The encoder performs convolution, while the decoder is responsible for deconvolution and un-pooling/up-sampling to predict pixel-wise class labels. The key idea is to apply the up-sampling decoder network, which maps the low-resolution encoder feature maps. This architecture substantially reduces the number of trainable parameters and reuses the encoder’s pooling indices to up-sample to map pixel-wise classification and segmentation. We have experimented with different activation functions, pooling methods, dropout units and architectures to design an efficient CNN architecture. The proposed network offers a significant improvement in performance in segmentation results while reducing the number of trainable parameters. Moreover, there is a considerable improvement in performance in comparison to the benchmark results over PASCAL VOC-12 and the CamVid. Full article