共查询到20条相似文献,搜索用时 15 毫秒
1.
Syedsafi Shajahan;Sriramakrishnan Pathmanaban;Kalaiselvi Tiruvenkadam; 《International journal of imaging systems and technology》2024,34(2):e23056
Glioma brain tumour is one of the life-threatening diseases in the world. Tumour substructure segmentation by physicians is a time-consuming task with the magnetic resonance imaging (MRI) technique due to the size of clinical data. An automatic and well-trained method is essential to detect and segment the tumour which increase the survival of the patients. The proposed work aims to produce high accuracy on glioma substructures segmentation with less computation time using deep learning. From the literature survey, the following challenges are found: (i) computing complex spatial boundaries between normal and tumour tissues, (ii) feature reduction and (iii) overfitting problems. Hence, we proposed a fully automatic glioma tumour segmentation using a residual-inception block (RIB) with a modified 3D U-Net (RIBM3DU-Net). It includes three phases: pre-processing, modified 3D U-Net segmentation and post-processing. From the results, RIB with U-Net enhances the segmentation accuracy. GPU parallel architecture reduces the computation time while training and testing. For quantitative analysis, comprehensive experiment results were computed and compared with state-of-the-art methods. It achieves better Dice scores on enhancing tumour, tumour core and complete tumour of 87%, 87% and 94%, respectively. GPU speedup folds yield up to 48× when compared with CPU. Quantitatively, 3D glioma volume is rendered from the obtained segmented results and estimated using the Cavalieri estimator. 相似文献
2.
Optical Coherence Tomography (OCT) is very important in medicine and provide useful diagnostic information. Measuring retinal layer thicknesses plays a vital role in pathophysiologic factors of many ocular conditions. Among the existing retinal layer segmentation approaches, learning or deep learning-based methods belong to the state-of-art. However, most of these techniques rely on manual-marked layers and the performances are limited due to the image quality. In order to overcome this limitation, we build a framework based on gray value curve matching, which uses depth learning to match the curve for semi-automatic segmentation of retinal layers from OCT. The depth convolution network learns the column correspondence in the OCT image unsupervised. The whole OCT image participates in the depth convolution neural network operation, compares the gray value of each column, and matches the gray value sequence of the transformation column and the next column. Using this algorithm, when a boundary point is manually specified, we can accurately segment the boundary between retinal layers. Our experimental results obtained from a 54-subjects database of both normal healthy eyes and affected eyes demonstrate the superior performances of our approach. 相似文献
3.
José Escorcia-Gutierrez Romany F. Mansour Kelvin Beleño Javier Jiménez-Cabas Meglys Pérez Natasha Madera Kevin Velasquez 《计算机、材料和连续体(英文)》2022,71(3):4221-4235
Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process. At the same time, breast cancer becomes the deadliest disease among women and can be detected by the use of different imaging techniques. Digital mammograms can be used for the earlier identification and diagnostic of breast cancer to minimize the death rate. But the proper identification of breast cancer has mainly relied on the mammography findings and results to increased false positives. For resolving the issues of false positives of breast cancer diagnosis, this paper presents an automated deep learning based breast cancer diagnosis (ADL-BCD) model using digital mammograms. The goal of the ADL-BCD technique is to properly detect the existence of breast lesions using digital mammograms. The proposed model involves Gaussian filter based pre-processing and Tsallis entropy based image segmentation. In addition, Deep Convolutional Neural Network based Residual Network (ResNet 34) is applied for feature extraction purposes. Specifically, a hyper parameter tuning process using chimp optimization algorithm (COA) is applied to tune the parameters involved in ResNet 34 model. The wavelet neural network (WNN) is used for the classification of digital mammograms for the detection of breast cancer. The ADL-BCD method is evaluated using a benchmark dataset and the results are analyzed under several performance measures. The simulation outcome indicated that the ADL-BCD model outperforms the state of art methods in terms of different measures. 相似文献
4.
Ore image segmentation is a key step in an ore grain size analysis based on image processing. The traditional segmentation methods do not deal with ore textures and shadows in ore images well Those methods often suffer from under-segmentation and over-segmentation. In this article, in order to solve the problem, an ore image segmentation method based on U-Net is proposed. We adjust the structure of U-Net to speed up the processing, and we modify the loss function to enhance the generalization of the model. After the collection of the ore image, we design the annotation standard and train the network with the annotated image. Finally, the marked watershed algorithm is used to segment the adhesion area. The experimental results show that the proposed method has the characteristics of fast speed, strong robustness and high precision. It has great practical value to the actual ore grain statistical task. 相似文献
5.
Yong Luo Xiaojie Li Chao Luo Feng Wang Xi Wu Imran Mumtaz Cheng Yi 《计算机、材料和连续体(英文)》2020,65(2):1771-1780
Tissue segmentation is a fundamental and important task in nasopharyngeal images analysis. However, it is a challenging task to accurately and quickly segment various tissues in the nasopharynx region due to the small difference in gray value between tissues in the nasopharyngeal image and the complexity of the tissue structure. In this paper, we propose a novel tissue segmentation approach based on a two-stage learning framework and U-Net. In the proposed methodology, the network consists of two segmentation modules. The first module performs rough segmentation and the second module performs accurate segmentation. Considering the training time and the limitation of computing resources, the structure of the second module is simpler and the number of network layers is less. In addition, our segmentation module is based on U-Net and incorporates a skip structure, which can make full use of the original features of the data and avoid feature loss. We evaluated our proposed method on the nasopharyngeal dataset provided by West China Hospital of Sichuan University. The experimental results show that the proposed method is superior to many standard segmentation structures and the recently proposed nasopharyngeal tissue segmentation method, and can be easily generalized across different tissue types in various organs. 相似文献
6.
Daniel Sierra-Sosa Sebastian Patino-Barrientos Begonya Garcia-Zapirain Cristian Castillo-Olea Adel Elmaghraby 《计算机、材料和连续体(英文)》2021,67(2):1629-1644
As colon cancer is among the top causes of death, there is a growing interest in developing improved techniques for the early detection of colon polyps. Given the close relation between colon polyps and colon cancer, their detection helps avoid cancer cases. The increment in the availability of colorectal screening tests and the number of colonoscopies have increased the burden on the medical personnel. In this article, the application of deep learning techniques for the detection and segmentation of colon polyps in colonoscopies is presented. Four techniques were implemented and evaluated: Mask-RCNN, PANet, Cascade R-CNN and Hybrid Task Cascade (HTC). These were trained and tested using CVC-Colon database, ETIS-LARIB Polyp, and a proprietary dataset. Three experiments were conducted to assess the techniques performance: 1) Training and testing using each database independently, 2) Mergingd the databases and testing on each database independently using a merged test set, and 3) Training on each dataset and testing on the merged test set. In our experiments, PANet architecture has the best performance in Polyp detection, and HTC was the most accurate to segment them. This approach allows us to employ Deep Learning techniques to assist healthcare professionals in the medical diagnosis for colon cancer. It is anticipated that this approach can be part of a framework for a semi-automated polyp detection in colonoscopies. 相似文献
7.
Fan Yang Jie Xu Haoliang Wei Meng Ye Mingzhu Xu Qiuru Fu Lingfei Ren Zhengwen Huang 《计算机、材料和连续体(英文)》2022,71(2):2963-2980
Zanthoxylum bungeanum Maxim, generally called prickly ash, is widely grown in China. Zanthoxylum rust is the main disease affecting the growth and quality of Zanthoxylum. Traditional method for recognizing the degree of infection of Zanthoxylum rust mainly rely on manual experience. Due to the complex colors and shapes of rust areas, the accuracy of manual recognition is low and difficult to be quantified. In recent years, the application of artificial intelligence technology in the agricultural field has gradually increased. In this paper, based on the DeepLabV2 model, we proposed a Zanthoxylum rust image segmentation model based on the FASPP module and enhanced features of rust areas. This paper constructed a fine-grained Zanthoxylum rust image dataset. In this dataset, the Zanthoxylum rust image was segmented and labeled according to leaves, spore piles, and brown lesions. The experimental results showed that the Zanthoxylum rust image segmentation method proposed in this paper was effective. The segmentation accuracy rates of leaves, spore piles and brown lesions reached 99.66%, 85.16% and 82.47% respectively. MPA reached 91.80%, and MIoU reached 84.99%. At the same time, the proposed image segmentation model also had good efficiency, which can process 22 images per minute. This article provides an intelligent method for efficiently and accurately recognizing the degree of infection of Zanthoxylum rust. 相似文献
8.
Sijing Cai;Yuwei Xiao;Yanyu Wang; 《International journal of imaging systems and technology》2024,34(1):e23023
With rapid developments in convolutional neural networks for image processing, deep learning methods based on pixel classification have been extensively applied in medical image segmentation. One popular strategy for such tasks is the encoder-decoder-based U-Net architecture and its variants. Most segmentation methods based on fully convolutional networks will cause the loss of spatial and contextual information due to continuous pooling operations or strided convolution when decreasing image resolution, and make less use of contextual information and global information under different receptive fields. To overcome this shortcoming, this paper proposes a novel structure called RAAU-Net. In our proposed RAAU-Net structure, which is a modified U-shaped architecture, we aim to capture high-level information while preserving spatial information and focusing on the regions of interest. RAAU-Net comprises three main components: a feature encoder module that utilizes a pre-trained ResNet-18 model as a fixed feature extractor, a multi-receptive field extraction module that we developed, and a feature decoder module. We have tested our method on several 2D medical image segmentation tasks such as retinal nerve, breast tumor, skin lesion, lung, gland, and polyp segmentation. All the indexes of the model reached the best in the dataset of skin lesions, in which Accuracy, Precision, IoU, Recall, and Dice Score were 3.26%, 5.42%, 9.92%, 6.52%, and 5.95% higher than UNet. 相似文献
9.
Yibo Wan;Gaofeng Wei;Renxing Li;Yifan Xiang;Dechao Yin;Minglei Yang;Deren Gong;Jiangang Chen; 《International journal of imaging systems and technology》2024,34(4):e23145
Accurate segmentation of retinal vessels is crucial for the early diagnosis and treatment of eye diseases, for example, diabetic retinopathy, glaucoma, and macular degeneration. Due to the intricate structure of retinal vessels, it is essential to extract their features with precision for the semantic segmentation of medical images. In this study, an improved deep learning neural network was developed with a focus on feature extraction based on the U-Net structure. The enhanced U-Net combines the architecture of convolutional neural networks (CNNs) with SE blocks (squeeze-and-excitation blocks) to adaptively extract image features after each U-Net encoder's convolution. This approach aids in suppressing nonvascular regions and highlighting features for specific segmentation tasks. The proposed method was trained and tested on the DRIVECHASE_DB1 and STARE datasets. As a result, the proposed model had an algorithmic accuracy, sensitivity, specificity, Dice coefficient (Dc), and Matthews correlation coefficient (MCC) of 95.62/0.9853/0.9652, 0.7751/0.7976/0.7773, 0.9832/0.8567/0.9865, 82.53/87.23/83.42, and 0.7823/0.7987/0.8345, respectively, outperforming previous methods, including UNet++, attention U-Net, and ResUNet. The experimental results demonstrated that the proposed method improved the retinal vessel segmentation performance. 相似文献
10.
针对现有基于Transformer的语义分割网络存在的多尺度语义信息利用不充分、处理图像时生成冗长序列导致的高计算成本等问题,本文提出了一种基于多尺度特征增强的高效语义分割主干网络MFE-Former。该网络主要包括多尺度池化自注意力模块(multi-scale pooling self-attention, MPSA)和跨空间前馈网络模块(cross-spatial feed-forward network, CS-FFN)。其中,MPSA利用多尺度池化操作对特征图序列进行降采样,在减少计算成本的同时还高效地从特征图序列中提取多尺度的上下文信息,增强Transformer对多尺度信息的建模能力;CS-FFN通过采用简化的深度卷积层替代传统的全连接层,减少前馈网络初始线性变换层的参数量,并在前馈网络中引入跨空间注意力(cross-spatial attention, CSA),使模型更有效地捕捉不同空间的交互信息,进一步增强模型的表达能力。MFE-Former在数据集ADE20K、Cityscapes和COCO-Stuff上的平均交并比分别达到44.1%、80.6%和38.0%,与主流分割算法相比,MFE-Former能够以更低的计算成本获得具有竞争力的分割精度,有效改善了现有方法多尺度信息利用不足和计算成本高的问题。
相似文献11.
R. S. Chithra P. Jagatheeswari 《International journal of imaging systems and technology》2020,30(4):994-1011
Tuberculosis (TB) is a highly infectious disease and is one of the major health problems all over the world. The accurate detection of TB is a major challenge faced by most of the existing methods. This work addresses these issues and developed an effective mechanism for detecting TB using deep learning. Here, the color space transformation is applied for transforming the red green and blue image to LUV space, where L stands for luminance, U and V represent chromaticity values of color images. Then, adaptive thresholding is carried out for image segmentation and various features, like coverage, density, color histogram, area, length, and texture features, are extracted to enable effective classification. After the feature extraction, the size of the features is reduced using principal component analysis. The extracted features are subjected to fractional crow search-based deep convolutional neural network (FC-SVNN) for the classification. Then, the image level features, like bacilli count, bacilli area, scattering coefficients and skeleton features are considered to perform severity detection using proposed adaptive fractional crow (AFC)-deep CNN. Finally, the inflection level is determined using entropy, density and detection percentage. The proposed AFC-Deep CNN algorithm is designed by modifying FC algorithm using self-adaptive concept. The proposed AFC-Deep CNN shows better performance with maximum accuracy value as 0.935. 相似文献
12.
Yongtao Shi;Wei Du;Chao Gao;Xinzhi Li; 《International journal of imaging systems and technology》2024,34(5):e23178
Accurately and rapidly segmenting the prostate in transrectal ultrasound (TRUS) images remains challenging due to the complex semantic information in ultrasound images. The paper discusses a cross-layer connection with SegFormer attention U-Net for efficient TRUS image segmentation. The SegFormer framework is enhanced by reducing model parameters and complexity without sacrificing accuracy. We introduce layer-skipping connections for precise positioning and combine local context with global dependency for superior feature recognition. The decoder is improved with Multi-layer Perceptual Convolutional Block Attention Module (MCBAM) for better upsampling and reduced information loss, leading to increased accuracy. The experimental results show that compared with classic or popular deep learning methods, this method has better segmentation performance, with the dice similarity coefficient (DSC) of 97.55% and the intersection over union (IoU) of 95.23%. This approach balances encoder efficiency, multi-layer information flow, and parameter reduction. 相似文献
13.
Xiangyu Deng;Zhiyan Dang;Lihao Pan; 《International journal of imaging systems and technology》2024,34(5):e23160
Thyroid nodules are a common endocrine system disorder for which accurate ultrasound image segmentation is important for evaluation and diagnosis, as well as a critical step in computer-aided diagnostic systems. However, the accuracy and consistency of segmentation remains a challenging task due to the presence of scattering noise, low contrast and resolution in ultrasound images. Therefore, we propose a deep learning-based CAD (computer-aided diagnosis) method, STU3Net in this paper, aiming at automatic segmentation of thyroid nodules. The method employs a modified Swin Transformer combined with a CNN encoder, which is capable of extracting morphological features and edge details of thyroid nodules in ultrasound images. In decoding through the features for image reconstruction, we introduce a modified three-layer U-Net network with cross-layer connectivity to further enhance image reduction. This cross-layer connectivity enhances the network's capture and representation of the contained image feature information by creating skip connections between different layers and merging the detailed information of the shallow network with the abstract information of the deeper network. Through comparison experiments with current mainstream deep learning methods on the TN3K and BUSI datasets, we validate the superiority of the STU3Net method in thyroid nodule segmentation performance. The experimental results show that STU3Net outperforms most of the mainstream models on the TN3K dataset, with Dice and IoU reaching 0.8368 and 0.7416, respectively, which are significantly better than other methods. The method demonstrates excellent performance on these datasets and provides radiologists with an effective auxiliary tool to accurately detect thyroid nodules in ultrasound images. 相似文献
14.
Shan Jin Hongming Xu Yue Dong Xinyu Hao Fengying Qin Qi Xu Yong Zhu Fengyu Cong 《International journal of imaging systems and technology》2023,33(1):362-377
Automatic cervical cancer segmentation in multimodal magnetic resonance imaging (MRI) is essential because tumor location and delineation can support patients' diagnosis and treatment planning. To meet this clinical demand, we present an encoder–decoder deep learning architecture which employs an EfficientNet encoder in the UNet++ architecture (E-UNet++). EfficientNet helps in effectively encoding multiscale image features. The nested decoders with skip connections aggregate multiscale features from low-level to high-level, which helps in detecting fine-grained details. A cohort of 228 cervical cancer patients with multimodal MRI sequences, including T2-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient imaging, contrast enhancement T1-weighted imaging, and dynamic contrast-enhanced imaging (DCE), has been explored. Evaluations are performed by considering either single or multimodal MRI with standard segmentation quantitative metrics: dice similarity coefficient (DSC), intersection over union (IOU), and 95% Hausdorff distance (HD). Our results show that the E-UNet++ model can achieve DSC values of 0.681–0.786, IOU values of 0.558–0.678, and 95% HD values of 3.779–7.411 pixels in different single sequences. Meanwhile, it provides DSC values of 0.644 and 0.687 on three DCE subsequences and all MRI sequences together. Our designed model is superior to other comparative models, which shows the potential to be used as an artificial intelligence tool for cervical cancer segmentation in multimodal MRI. 相似文献
15.
Suting Peng Wei Chen Jiawei Sun Boqiang Liu 《International journal of imaging systems and technology》2020,30(1):5-17
Gliomas segmentation is a critical and challenging task in surgery and treatment, and it is also the basis for subsequent evaluation of gliomas. Magnetic resonance imaging is extensively employed in diagnosing brain and nervous system abnormalities. However, brain tumor segmentation remains a challenging task, because differentiating brain tumors from normal tissues is difficult, tumor boundaries are often ambiguous and there is a high degree of variability in the shape, location, and extent of the patient. It is therefore desired to devise effective image segmentation architectures. In the past few decades, many algorithms for automatic segmentation of brain tumors have been proposed. Methods based on deep learning have achieved favorable performance for brain tumor segmentation. In this article, we propose a Multi-Scale 3D U-Nets architecture, which uses several U-net blocks to capture long-distance spatial information at different resolutions. We upsample feature maps at different resolutions to extract and utilize sufficient features, and we hypothesize that semantically similar features are easier to learn and process. In order to reduce the computational cost, we use 3D depthwise separable convolution instead of some standard 3D convolution. On BraTS 2015 testing set, we obtained dice scores of 0.85, 0.72, and 0.61 for the whole tumor, tumor core, and enhancing tumor, respectively. Our segmentation performance was competitive compared to other state-of-the-art methods. 相似文献
16.
Syed Sajid Hussain Jainy Sachdeva Chirag Kamal Ahuja Abhiav Singh 《International journal of imaging systems and technology》2023,33(2):465-482
The diagnosis' treatment planning, follow-up and prognostication of Gliomas is significantly enhanced on Magnetic Resonance Imaging. In the present research, deep learning-based variant of convolutional neural network methodology is proposed for glioma segmentation where pretrained autoencoder acts as backbone to the 3D-Unet which performs the segmentation task as well as image restoration. Further, Unet accepts input as the combination of three non-native MR images (T2, T1CE, and FLAIR) to extract maximum and superior features for segmenting tumor regions. Further, weighted dice loss employed, focusses on segregating tumor region into three regions of interest namely whole tumor with oedema (WT), enhancing tumor (ET), and tumor core (TC). The optimizer preferred in the proposed methodology is Adam and the learning rate is initially set to , progressively reduced by a cosine decay after 50 epochs. The learning parameters are reduced to a larger extent (up to 9.8 M as compared to 27 M). The experimental results show that the proposed model achieved Dice similarity coefficients: 0.77, 0.92, and 0.84; sensitivity: 0.90, 0.95, and 0.89; specificity: 0.97, 0.99, and 0.99; Hausdorff95: 5.74, 4.89, and 6.00, in the three regions including ET, WT, TC. This proposed Glioma segmentation method is efficient for segregation of tumors. 相似文献
17.
XIE Ning;CHEN Liang;PEI Ziqing;HE Zhicheng;CHEN Tao 《测试技术学报》2024,38(1):12-18
Aiming at the harsh environment and serious light pollution in the production workshop of automobile body-in-white, it is difficult to accurately locate and inefficient when the vision system and other equipment are combined to detect the quality of the solder joints. An improved U-Net image segmentation algorithm was proposed. By improving the convolution structure to better fuse the semantic information of the feature map and lighten the network structure. Improve the loss function and integrate the attention mechanism to better mine the foreground in the case of uneven positive and negative samples, obtain spatial features of different scale feature maps and establish long-term channel relationships. Compared with the original U-Net network, the Dice coefficient of the proposed RPSA-U-Net network is increased by 8.76% to 0.983 6, the MIOU is increased by 11.5% to 0.967 81, and the network parameters are also reduced by 7%. Combined with the image processing method to find the center of the solder joint, the efficiency is higher and the precision is higher, and it has application value. 相似文献
18.
Yong-Woon Kim Yung-Cheol Byun Dong Seog Han Dalia Dominic Sibu Cyriac 《计算机、材料和连续体(英文)》2022,73(3):4743-4762
A wide range of camera apps and online video conferencing services support the feature of changing the background in real-time for aesthetic, privacy, and security reasons. Numerous studies show that the Deep-Learning (DL) is a suitable option for human segmentation, and the ensemble of multiple DL-based segmentation models can improve the segmentation result. However, these approaches are not as effective when directly applied to the image segmentation in a video. This paper proposes an Adaptive N-Frames Ensemble (AFE) approach for high-movement human segmentation in a video using an ensemble of multiple DL models. In contrast to an ensemble, which executes multiple DL models simultaneously for every single video frame, the proposed AFE approach executes only a single DL model upon a current video frame. It combines the segmentation outputs of previous frames for the final segmentation output when the frame difference is less than a particular threshold. Our method employs the idea of the N-Frames Ensemble (NFE) method, which uses the ensemble of the image segmentation of a current video frame and previous video frames. However, NFE is not suitable for the segmentation of fast-moving objects in a video nor a video with low frame rates. The proposed AFE approach addresses the limitations of the NFE method. Our experiment uses three human segmentation models, namely Fully Convolutional Network (FCN), DeepLabv3, and Mediapipe. We evaluated our approach using 1711 videos of the TikTok50f dataset with a single-person view. The TikTok50f dataset is a reconstructed version of the publicly available TikTok dataset by cropping, resizing and dividing it into videos having 50 frames each. This paper compares the proposed AFE with single models and the Two-Models Ensemble, as well as the NFE models. The experiment results show that the proposed AFE is suitable for low-movement as well as high-movement human segmentation in a video. 相似文献
19.
S. S. Kumar;R. S. Vinod Kumar; 《International journal of imaging systems and technology》2024,34(4):e23126
Liver segmentation is a crucial step in medical image analysis and is essential for diagnosing and treating liver diseases. However, manual segmentation is time-consuming and subject to variability among observers. To address these challenges, a novel liver segmentation approach, SwinUNet with transformer skip-fusion is proposed. This method harnesses the Swin Transformer's capacity to model long-range dependencies efficiently, the U-Net's ability to preserve fine spatial details, and the transformer skip-fusion's effectiveness in enabling the decoder to learn intricate features from encoder feature maps. In experiments using the 3DIRCADb and CHAOS datasets, this technique outperformed traditional CNN-based methods, achieving a mean DICE coefficient of 0.988% and a mean Jaccard coefficient of 0.973% by aggregating the results obtained from each dataset, signifying outstanding agreement with ground truth. This remarkable accuracy in liver segmentation holds significant promise for improving liver disease diagnosis and enhancing healthcare outcomes for patients with liver conditions. 相似文献
20.
Ningcheng Yuan Chao Jia Jizhao Lu Shaoyong Guo Wencui Li Xuesong Qiu Lei Shi 《计算机、材料和连续体(英文)》2020,64(3):1657-1671
Container is an emerging virtualization technology and widely adopted in the cloud to provide services because of its lightweight, flexible, isolated and highly portable properties. Cloud services are often instantiated as clusters of interconnected containers. Due to the stochastic service arrival and complicated cloud environment, it is challenging to achieve an optimal container placement (CP) scheme. We propose to leverage Deep Reinforcement Learning (DRL) for solving CP problem, which is able to learn from experience interacting with the environment and does not rely on mathematical model or prior knowledge. However, applying DRL method directly dose not lead to a satisfying result because of sophisticated environment states and huge action spaces. In this paper, we propose UNREAL-CP, a DRL-based method to place container instances on servers while considering end to end delay and resource utilization cost. The proposed method is an actor-critic-based approach, which has advantages in dealing with the huge action space. Moreover, the idea of auxiliary learning is also included in our architecture. We design two auxiliary learning tasks about load balancing to improve algorithm performance. Compared to other DRL methods, extensive simulation results show that UNREAL-CP performs better up to 28.6% in terms of reducing delay and deployment cost with high training efficiency and responding speed. 相似文献