Cascade R-CNN Research Articles

Improved Cascade-RCNN for automatic detection of coronary artery plaque in multi-angle fusion CPR images

Coronary heart disease is a disease that seriously endangers human health and life which is caused by plaque formation due to coronary artery atherosclerosis or spasm. The detection of coronary plaques through medical imaging is a non-destructive and fast diagnostic method, which holds significant medical and clinical value for the diagnosis of coronary heart disease. In this paper, We proposed an automatic detecting method for coronary artery vulnerable plaque based on CTA image sequence. We collected CTA image sequences from 132 patients and obtained the coronary artery tree by performing three-dimensional (3D) segmentation. The coronary artery centerline was extracted using the skeleton thinning method. To address the issue of insufficient features in a single CPR image, we constructed Curved Planar Reconstructed (CPR) images from multiple directions perpendicular to the coronary artery, following the centerline. In total, we established 8797 images in the training dataset and 1553 images in the test dataset. We proposed an attentive detection method that fused a new Cascade RCNN ResNet50-FPN neural network model with a convolutional attention module (CBAM). This method achieved automatic recognition and detection of plaques on the two-dimensional multi-angle fused CPR images. The proposed ResNet50-FPN network performed feature extraction, and CBAM was added after the first and last convolutional layers to enhance the network’s attention to plaque features. Our experimental results demonstrate that deep learning neural networks can provide a feasible approach to automatically detecting coronary artery plaques. Our method achieved a plaque detection accuracy of 94.6%, outperforming the RCNN method proposed by Zriek.

RT-SNDETR: real-time supernova detection via end-to-end image transformers

ABSTRACT In large-scale astronomical surveys, traditional supernova detection pipelines rely on complex and relatively inefficient image differencing techniques. This paper proposes an end-to-end deep-learning supernova detection network, the Real-Time SuperNova DEtection TRansformer (RT-SNDETR). This network partially replaces traditional pipelines by integrating image differencing, source detection, and Real-bogus classification, achieving a speed 51.49 times that of the fastest image differencing method, SFFT. Additionally, it remains competitive with methods like YOLO v8, offering a well-balanced trade-off between speed and accuracy. Experimental results highlight RT-SNDETR’s superior performance, with an average precision(AP) of 96.30 per cent on synthetic samples and 76.60 per cent on real supernova samples. It significantly outperforms other detection networks, including RT-DETR (+5.6 per cent AP on synthetic/+5.1 per cent AP on real samples) and Cascade R-CNN (+8.9 per cent AP on synthetic/ +28.6 per cent AP on real samples). The incorporation of CycleGAN-based data generation methods plays a significant role in enhancing RT-SNDETR’s performance. These methods simulate realistic PSF variations, enabling the object detection network to learn more robust features and improving its generalization to real supernovae data. Additionally, by integrating unsupervised domain adaptation techniques, RT-SNDETR achieves an AP of 81.70 per cent on real SDSS supernova survey samples. This study demonstrates RT-SNDETR’s potential to significantly enhance both the speed and accuracy of supernova detection, making it a highly effective solution for large-scale astronomical surveys.

Thermal Battery Multi-Defects Detection and Discharge Performance Analysis Based on Computed Tomography Imaging

To address the typical structural defects that are prone to occur during the preparation and storage processes of thermal battery, experiments of battery image acquisition were designed based on X-ray computed tomography system. An improved Yolov5s network was employed to achieve high-precision automatic detection of typical defects. Through the discharge experiment of thermal battery, discharge performance curves of normal batteries and three defective batteries were constructed. The impact and mechanisms of different defects on the discharge performance were analyzed based on the voltage curve. By designing an automatic stitching scheme, the phenomenon of interlayer information overlap caused by the increase of cone angle in digital radiography images was suppressed. To address the issues of low image contrast and limited defect data in thermal battery imaging, the defect dataset was expanded using the designed image preprocessing steps and improving the contrast of the images. For subtle defects that are difficult to identify, the introduced multi-head self-attention mechanism in Transformer and the use of Focal Loss instead of cross-entropy loss function were employed to improve the recognition accuracy of subtle defects while ensuring the detection speed. The comparative experiment shows that the improved network model has higher recognition accuracy compared to Faster R-CNN, SSD, Cascade R-CNN, EfficientDet and the original Yolov5s network. The recognition accuracy of typical defects in thermal batteries can reach 98.7%.

Attentive context and semantic enhancement mechanism for printed circuit board defect detection with two-stage and multi-stage object detectors

Printed Circuit Boards (PCBs) are key devices for the modern-day electronic technologies. During the production of these boards, defects may occur. Several methods have been proposed to detect PCB defects. However, detecting significantly smaller and visually unrecognizable defects has been a long-standing challenge. The existing two-stage and multi-stage object detectors that use only one layer of the backbone, such as Resnet’s third layer (C4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$C_4$$\\end{document}) or fourth layer (C5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$C_5$$\\end{document}), suffer from low accuracy, and those that use multi-layer feature maps extractors, such as Feature Pyramid Network (FPN), incur higher computational cost. Founded by these challenges, we propose a robust, less computationally intensive, and plug-and-play Attentive Context and Semantic Enhancement Module (ACASEM) for two-stage and multi-stage detectors to enhance PCB defects detection. This module consists of two main parts, namely adaptable feature fusion and attention sub-modules. The proposed model, ACASEM, takes in feature maps from different layers of the backbone and fuses them in a way that enriches the resulting feature maps with more context and semantic information. We test our module with state-of-the-art two-stage object detectors, Faster R-CNN and Double-Head R-CNN, and with multi-stage Cascade R-CNN detector on DeepPCB and Augmented PCB Defect datasets. Empirical results demonstrate improvement in the accuracy of defect detection.

A Study on Comparative Analysis of Object Detection-based Real-Time Wildfire and Class B Fire Detection Performance

Wildfires cause human casualties, property damage, and significant damage to ecosystems, so prompt fire detection and response are essential. In this study, training and evaluation experiments were performed on the YOLOv3, Faster R-CNN, and Cascade R-CNN models to analyze their real-time wildfire detection performance. The performance of each model was evaluated using precision, recall, mAP 50, mAP 50-95, the model parameter, and frames per second (FPS). The experiment results showed that YOLOv3 had slightly lower overall performance than other models, but the difference was not significant in terms of mAP 50. YOLOv3 had the highest inference speed (48.7 FPS) and an overall mAP 50 of 0.832. On the contrary, Faster R-CNN's overall mAP 50 was 0.835, while Cascade R-CNN showed higher performance with 0.840. Compared to YOLOv3, both models showed relatively low inference speeds (36.2 and 26.3 FPS, respectively). Therefore, the YOLOv3 model is deemed appropriate for real-time fire detection owing to its high inference speed and insignificant wildfire detection performance difference in terms of mAP 50. Future wildfire detection models are expected to improve by optimizing the network structures (e.g., FPN and Head) of 1-Stage models, such as YOLOv3.

Enhanced Cascade R-CNN for Multiscale Object Detection in Dense Scenes From SAR Images

Enhanced Cascade R-CNN for Multiscale Object Detection in Dense Scenes From SAR Images

Farmland pest recognition based on Cascade RCNN Combined with Swin-Transformer

Farmland pest recognition based on Cascade RCNN Combined with Swin-Transformer

Intelligent field monitoring system for cruciferous vegetable pests using yellow sticky trap images and an improved Cascade R-CNN

Cruciferous vegetables are important edible vegetable crops. However, they are susceptible to various pests during their growth process, which requires real-time and accurate monitoring of these pests for pest forecasting and scientific control. Hanging yellow sticky boards are a common way to monitor and trap those pests which are attracted to the yellow color. To achieve real-time, low-cost, intelligent monitoring of these vegetable pests on the boards, we established an intelligent monitoring system consisting of a smart camera, a web platform and a pest detection algorithm deployed on a server. After the operator sets the monitoring preset points and shooting time of the camera on the system platform, the camera in the field can automatically collect images of multiple yellow sticky boards at fixed places and times every day. The pests trapped on the yellow sticky boards in vegetable fields, Plutella xylostella, Phyllotreta striolata and flies, are very small and susceptible to deterioration and breakage, which increases the difficulty of model detection. To solve the problem of poor recognition due to the small size and breaking of the pest bodies, we propose an intelligent pest detection algorithm based on an improved Cascade R-CNN model for three important cruciferous crop pests. The algorithm uses an overlapping sliding window method, an improved Res2Net network as the backbone network, and a recursive feature pyramid network as the neck network. The results of field tests show that the algorithm achieves good detection results for the three target pests on the yellow sticky board images, with precision levels of 96.5, 92.2 and 75.0%, and recall levels of 96.6, 93.1 and 74.7%, respectively, and an F1 value of 0.880. Compared with other algorithms, our algorithm has a significant advantage in its ability to detect small target pests. To accurately obtain the data for the newly added pests each day, a two-stage pest matching algorithm was proposed. The algorithm performed well and achieved results that were highly consistent with manual counting, with a mean error of only 2.2%. This intelligent monitoring system realizes precision, good visualization, and intelligent vegetable pest monitoring, which is of great significance as it provides an effective pest prevention and control option for farmers.

Comparative analysis of computer vision algorithms for the real-time detection of digital dermatitis in dairy cows

Digital dermatitis (DD) is a bovine claw disease responsible for ulcerative lesions on the planar aspect of the hoof. DD is associated with massive herd outbreaks of lameness and influences cattle welfare and production. Early detection of DD can lead to prompt treatment and decrease lameness. Computer vision (CV) provides a unique opportunity to improve early detection. The study aims to train and compare applications for the real-time detection of DD in dairy cows. Eight CV models were trained for detection and scoring, compared using performance metrics and inference time, and the best model was automated for real-time detection using images and video. Images were collected from commercial dairy farms while facing the interdigital space on the plantar surface of the foot. Images were scored for M-stages of DD by a trained investigator using the M-stage DD classification system with distinct labels for hyperkeratosis (H) and proliferations (P). Two sets of images were compiled: the first dataset (Dataset 1) containing 1,177 M0/M4H and 1,050 M2/M2P images and the second dataset (Dataset 2) containing 240 M0, 17 M2, 51 M2P, 114 M4H, and 108 M4P images. Models were trained to detect and score DD lesions and compared for precision, recall, and mean average precision (mAP) in addition to inference time in frame per second (FPS). Seven of the nine CV models performed well compared to the ground truth of labeled images using Dataset 1. The six models, Faster R-CNN, Cascade R-CNN, YOLOv3, Tiny YOLOv3, YOLOv4, Tiny YOLOv4, and YOLOv5s achieved an mAP between 0.964 and 0.998, whereas the other two models, SSD and SSD Lite, yielded an mAP of 0.371 and 0.387 respectively. Overall, YOLOv4, Tiny YOLOv4, and YOLOv5s outperformed all other models with almost perfect precision, perfect recall, and a higher mAP. Tiny YOLOv4 outperformed all other models with respect to inference time at 333 FPS, followed by YOLOv5s at 133 FPS and YOLOv4 at 65 FPS. YOLOv4 and Tiny YOLOv4 performed better than YOLOv5s compared to the ground truth using Dataset 2. YOLOv4 and Tiny YOLOv4 yielded a similar mAP of 0.896 and 0.895, respectively. However, Tiny YOLOv4 achieved both higher precision and recall compared to YOLOv4. Finally, Tiny YOLOv4 was able to detect DD lesions on a commercial dairy farm with high performance and speed. The proposed CV tool can be used for early detection and prompt treatment of DD in dairy cows. This result is a step towards applying CV algorithms to veterinary medicine and implementing real-time DD detection on dairy farms.

Deep-learning-based sampling position selection on color Doppler sonography images during renal artery ultrasound scanning

Accurate selection of sampling positions is critical in renal artery ultrasound examinations, and the potential of utilizing deep learning (DL) for assisting in this selection has not been previously evaluated. This study aimed to evaluate the effectiveness of DL object detection technology applied to color Doppler sonography (CDS) images in assisting sampling position selection. A total of 2004 patients who underwent renal artery ultrasound examinations were included in the study. CDS images from these patients were categorized into four groups based on the scanning position: abdominal aorta (AO), normal renal artery (NRA), renal artery stenosis (RAS), and intrarenal interlobular artery (IRA). Seven object detection models, including three two-stage models (Faster R-CNN, Cascade R-CNN, and Double Head R-CNN) and four one-stage models (RetinaNet, YOLOv3, FoveaBox, and Deformable DETR), were trained to predict the sampling position, and their predictive accuracies were compared. The Double Head R-CNN model exhibited significantly higher average accuracies on both parameter optimization and validation datasets (89.3 ± 0.6% and 88.5 ± 0.3%, respectively) compared to other methods. On clinical validation data, the predictive accuracies of the Double Head R-CNN model for all four types of images were significantly higher than those of the other methods. The DL object detection model shows promise in assisting inexperienced physicians in improving the accuracy of sampling position selection during renal artery ultrasound examinations.

Multi-scale coupled attention for visual object detection.

The application of deep neural network has achieved remarkable success in object detection. However, the network structures should be still evolved consistently and tuned finely to acquire better performance. This gears to the continuous demands on high performance in those complex scenes, where multi-scale objects to be detected are located here and there. To this end, this paper proposes a network structure called Multi-Scale Coupled Attention (MSCA) under the framework of self-attention learning with methodologies of importance assessment. Architecturally, it consists of a Multi-Scale Coupled Channel Attention (MSCCA) module, and a Multi-Scale Coupled Spatial Attention (MSCSA) module. Specifically, the MSCCA module is developed to achieve the goal of self-attention learning linearly on the multi-scale channels. In parallel, the MSCSA module is constructed to achieve this goal nonlinearly on the multi-scale spatial grids. The MSCCA and MSSCA modules can be connected together into a sequence, which can be used as a plugin to develop end-to-end learning models for object detection. Finally, our proposed network is compared on two public datasets with 13 classical or state-of-the-art models, including the Faster R-CNN, Cascade R-CNN, RetinaNet, SSD, PP-YOLO, YOLO v3, YOLO v5, YOLO v7, YOLOX, DETR, conditional DETR, UP-DETR and FP-DETR. Comparative experimental results with numerical scores, the ablation study, and the performance behaviour all demonstrate the effectiveness of our proposed model.

Research on Remote-Sensing Identification Method of Typical Disaster-Bearing Body Based on Deep Learning and Spatial Constraint Strategy

The census and management of hazard-bearing entities, along with the integrity of data quality, form crucial foundations for disaster risk assessment and zoning. By addressing the challenge of feature confusion, prevalent in single remotely sensed image recognition methods, this paper introduces a novel method, Spatially Constrained Deep Learning (SCDL), that combines deep learning with spatial constraint strategies for the extraction of disaster-bearing bodies, focusing on dams as a typical example. The methodology involves the creation of a dam dataset using a database of dams, followed by the training of YOLOv5, Varifocal Net, Faster R-CNN, and Cascade R-CNN models. These models are trained separately, and highly confidential dam location information is extracted through parameter thresholding. Furthermore, three spatial constraint strategies are employed to mitigate the impact of other factors, particularly confusing features, in the background region. To assess the method’s applicability and efficiency, Qinghai Province serves as the experimental area, with dam images from the Google Earth Pro database used as validation samples. The experimental results demonstrate that the recognition accuracy of SCDL reaches 94.73%, effectively addressing interference from background factors. Notably, the proposed method identifies six dams not recorded in the GOODD database, while also detecting six dams in the database that were previously unrecorded. Additionally, four dams misdirected in the database are corrected, contributing to the enhancement and supplementation of the global dam geo-reference database and providing robust support for disaster risk assessment. In conclusion, leveraging open geographic data products, the comprehensive framework presented in this paper, encompassing deep learning target detection technology and spatial constraint strategies, enables more efficient and accurate intelligent retrieval of disaster-bearing bodies, specifically dams. The findings offer valuable insights and inspiration for future advancements in related fields.

PVswin-YOLOv8s: UAV-Based Pedestrian and Vehicle Detection for Traffic Management in Smart Cities Using Improved YOLOv8

In smart cities, effective traffic congestion management hinges on adept pedestrian and vehicle detection. Unmanned Aerial Vehicles (UAVs) offer a solution with mobility, cost-effectiveness, and a wide field of view, and yet, optimizing recognition models is crucial to surmounting challenges posed by small and occluded objects. To address these issues, we utilize the YOLOv8s model and a Swin Transformer block and introduce the PVswin-YOLOv8s model for pedestrian and vehicle detection based on UAVs. Firstly, the backbone network of YOLOv8s incorporates the Swin Transformer model for global feature extraction for small object detection. Secondly, to address the challenge of missed detections, we opt to integrate the CBAM into the neck of the YOLOv8. Both the channel and the spatial attention modules are used in this addition because of how well they extract feature information flow across the network. Finally, we employ Soft-NMS to improve the accuracy of pedestrian and vehicle detection in occlusion situations. Soft-NMS increases performance and manages overlapped boundary boxes well. The proposed network reduced the fraction of small objects overlooked and enhanced model detection performance. Performance comparisons with different YOLO versions ( for example YOLOv3 extremely small, YOLOv5, YOLOv6, and YOLOv7), YOLOv8 variants (YOLOv8n, YOLOv8s, YOLOv8m, and YOLOv8l), and classical object detectors (Faster-RCNN, Cascade R-CNN, RetinaNet, and CenterNet) were used to validate the superiority of the proposed PVswin-YOLOv8s model. The efficiency of the PVswin-YOLOv8s model was confirmed by the experimental findings, which showed a 4.8% increase in average detection accuracy (mAP) compared to YOLOv8s on the VisDrone2019 dataset.

Automatic Detection of Rice Blast Fungus Spores by Deep Learning-Based Object Detection: Models, Benchmarks and Quantitative Analysis

The severity of rice blast and its impacts on rice yield are closely related to the inoculum quantity of Magnaporthe oryzae, and automatic detection of the pathogen spores in microscopic images can provide a rapid and effective way to quantify pathogen inoculum. Traditional spore detection methods mostly rely on manual feature extraction and shallow machine learning models, and are mostly designed for the indoor counting of a single spore class, which cannot handle the interference of impurity particles in the field. This study achieved automatic detection of rice blast fungus spores in the mixture with other fungal spores and rice pollens commonly encountered under field conditions by using deep learning based object detection techniques. First, 8959 microscopic images of a single spore class and 1450 microscopic images of mixed spore classes, including the rice blast fungus spores and four common impurity particles, were collected and labelled to form the benchmark dataset. Then, Faster R-CNN, Cascade R-CNN and YOLOv3 were used as the main detection frameworks, and multiple convolutional neural networks were used as the backbone networks in training of nine object detection algorithms. The results showed that the detection performance of YOLOv3_DarkNet53 is superior to the other eight algorithms, and achieved 98.0% mean average precision (intersection over union > 0.5) and an average speed of 36.4 frames per second. This study demonstrated the enormous application potential of deep object detection algorithms in automatic detection and quantification of rice blast fungus spores.

IMU-CNN: implementing remote sensing image restoration framework based on Mask-Upgraded Cascade R-CNN and deep autoencoder

IMU-CNN: implementing remote sensing image restoration framework based on Mask-Upgraded Cascade R-CNN and deep autoencoder

Benchmarking wild bird detection in complex forest scenes

Camera traps are widely used for wildlife monitoring and making informed conservation and land-management decisions, but the resulting ‘big data’ are laborious to process. Deep learning-based methods have been adopted for wildlife detection in camera traps. However, these methods detect large mammals in uncomplicated scenes, where powerful deep-learning models work effectively. Few studies have been conducted to develop artificial intelligence for recognizing wild birds that live in complicated field scenes with protective colors and small sizes. Here we used a dataset of 9717 images from 15 bird species based on camera traps to test 8 object detection algorithms (Faster RCNN, Cascade RCNN, RetinaNet, FCOS, RepPoints, ATSS, Deformable-DETR, and Sparse RCNN) and assess their performance. We also explored the effect of different backbones on model accuracy. Among them, the Cascade RCNN model performs best, with a mAP of 0.693 in model capabilities. Models perform differently in certain species, and backbones significantly affect the accuracy of the model. Cascade RCNN utilizing the Swin-T backbone is the best-performing combination, with a mAP of 0.704. This study could help researchers identify birds efficiently and inspires research on wildlife recognition in complex ecological settings.

Distilling object detectors with efficient logit mimicking and mask-guided feature imitation

Knowledge distillation (KD) is a promising approach to learning compact models for object detection with information inherited from intricate teacher networks. In this paper, we raise some shortcomings of existing KD methods for object detectors, e.g., ignoring knowledge selection, coarse feature imitation mask, etc. To address these issues, a novel KD framework has been presented to train efficient object detectors via Logit Mimicking and Feature Imitation (LMFI). First, a novel logit mimicking method is put forward to distill classification and localization heads. On the one hand, it first proposes to mimic the classification logits of one-category object detectors. On the other hand, the localization knowledge from teacher predictions and ground truths are exploited, which dynamically guides the learning of student’s regression outputs by stages. Second, an adaptive positive teacher selection (APTS) strategy is designed to obtain high-quality teacher samples during distillation, which reduces the transmission of inferior knowledge. Moreover, a soft metric and a fine-grained mask are heuristically introduced to reconcile the discrepancy between teacher and student features in a position-wise manner. Extensive experiments show that LMFI outperforms the state-of-the-art KD frameworks for object detection. It can significantly boost the performance of various detectors on different benchmarks, e.g., 2.83% and 2.58% MR−2 reduction of Cascade R-CNN on the S and All sets of CityPersons, and an improvement of ResNet-50 based TOOD from 40.3% to 42.9% mAP on the COCO benchmark.

Morphology-based deep learning enables accurate detection of senescence in mesenchymal stem cell cultures.

Cell senescence is a sign of aging and plays a significant role in the pathogenesis of age-related disorders. For cell therapy, senescence may compromise the quality and efficacy of cells, posing potential safety risks. Mesenchymal stem cells (MSCs) are currently undergoing extensive research for cell therapy, thus necessitating the development of effective methods to evaluate senescence. Senescent MSCs exhibit distinctive morphology that can be used for detection. However, morphological assessment during MSC production is often subjective and uncertain. New tools are required for the reliable evaluation of senescent single cells on a large scale in live imaging of MSCs. We have developed a successful morphology-based Cascade region-based convolution neural network (Cascade R-CNN) system for detecting senescent MSCs, which can automatically locate single cells of different sizes and shapes in multicellular images and assess their senescence state. Additionally, we tested the applicability of the Cascade R-CNN system for MSC senescence and examined the correlation between morphological changes with other senescence indicators. This deep learning has been applied for the first time to detect senescent MSCs, showing promising performance in both chronic and acute MSC senescence. The system can be a labor-saving and cost-effective option for screening MSC culture conditions and anti-aging drugs, as well as providing a powerful tool for non-invasive and real-time morphological image analysis integrated into cell production.

Automated Quantification of HER2 Amplification Levels Using Deep Learning.

HER2 assessment is necessary for patient selection in anti-HER2 targeted treatment. However, manual assessment of HER2 amplification is time-costly, labor-intensive, highly subjective and error-prone. Challenges in HER2 analysis in fluorescence in situ hybridization (FISH) and dual in situ hybridization (DISH) images include unclear and blurry cell boundaries, large variations in cell shapes and signals, overlapping and clustered cells and sparse label issues with manual annotations only on cells with high confidences, producing subjective assessment scores according to the individual choices on cell selection. To address the above-mentioned issues, we have developed a soft-sampling cascade deep learning model and a signal detection model in quantifying CEN17 and HER2 of cells to assist assessment of HER2 amplification status for patient selection of HER2 targeting therapy to breast cancer. In evaluation with two different kinds of clinical datasets, including a FISH data set and a DISH data set, the proposed method achieves high accuracy, recall and F1-score for both datasets in instance segmentation of HER2 related cells that must contain both CEN17 and HER2 signals. Moreover, the proposed method is demonstrated to significantly outperform seven state of the art recently published deep learning methods, including contour proposal network (CPN), soft label-based FCN (SL-FCN), modified fully convolutional network (M-FCN), bilayer convolutional network (BCNet), SOLOv2, Cascade R-CNN and DeepLabv3+ with three different backbones (p ≤ 0.01). Clinically, anti-HER2 therapy can also be applied to gastric cancer patients. We applied the developed model to assist in HER2 DISH amplification assessment for gastric cancer patients, and it also showed promising predictive results (accuracy 97.67 ±1.46%, precision 96.15 ±5.82%, respectively).

A new double attention decoding model based on cascade RCNN and word embedding fusion for Chinese-English multimodal translation

A new double attention decoding model based on cascade RCNN and word embedding fusion for Chinese-English multimodal translation