Scheme Based on Multi-Level Patch Attention and Lesion Localization for Diabetic Retinopathy Grading

Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional attention schemes have not considered the impact of lesion type differences on grading,resulting in unreasonable extraction of important lesion features.Therefore,this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator(MPAG)and a lesion localization module(LLM).Firstly,MPAGis used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained in the patches,fully considering the impact of lesion type differences on grading,solving the problem that the attention maps of lesions cannot be further refined and then adapted to the final DR diagnosis task.Secondly,the LLM generates a global attention map based on localization.Finally,the weighted attention map and global attention map are weighted with the fundus map to fully explore effective DR lesion information and increase the attention of the classification network to lesion details.This paper demonstrates the effectiveness of the proposed method through extensive experiments on the public DDR dataset,obtaining an accuracy of 0.8064.

Spatial and Contextual Path Network for Image Inpainting

Image inpainting is a kind of use known area of information technology to repair the loss or damage to the area.Image feature extraction is the core of image restoration.Getting enough space for information and a larger receptive field is very important to realize high-precision image inpainting.However,in the process of feature extraction,it is difficult to meet the two requirements of obtaining sufficient spatial information and large receptive fields at the same time.In order to obtain more spatial information and a larger receptive field at the same time,we put forward a kind of image restoration based on space path and context path network.For the space path,we stack three convolution layers for 1/8 of the figure,the figure retained the rich spatial details.For the context path,we use the global average pooling layer,where the accept field is the maximum of the backbone network,and the pooling module can provide global context information for the maximum accept field.In order to better integrate the features extracted from the spatial and contextual paths,we study the fusion module of the two paths.Features fusionmodule first path output of the space and context path,and then through themass normalization to balance the scale of the characteristics,finally the characteristics of the pool will be connected into a feature vector and calculate the weight vector.Features of images in order to extract context information,we add attention to the context path refinement module.Attention modules respectively from channel dimension and space dimension to weighted images,in order to obtain more effective information.Experiments show that our method is better than the existing technology in the quality and quantity of themethod,and further to expand our network to other inpainting networks,in order to achieve consistent performance improvements.

A PSO based Energy Efficient Coverage Control Algorithm for Wireless Sensor Networks

Wireless Sensor Networks(WSNs)are large-scale and high-density networks that typically have coverage area overlap.In addition,a random deployment of sensor nodes cannot fully guarantee coverage of the sensing area,which leads to coverage holes in WSNs.Thus,coverage control plays an important role in WSNs.To alleviate unnecessary energy wastage and improve network performance,we consider both energy efficiency and coverage rate for WSNs.In this paper,we present a novel coverage control algorithm based on Particle Swarm Optimization(PSO).Firstly,the sensor nodes are randomly deployed in a target area and remain static after deployment.Then,the whole network is partitioned into grids,and we calculate each grid’s coverage rate and energy consumption.Finally,each sensor nodes’sensing radius is adjusted according to the coverage rate and energy consumption of each grid.Simulation results show that our algorithm can effectively improve coverage rate and reduce energy consumption.

A Real-Time and Ubiquitous Network Attack Detection Based on Deep Belief Network and Support Vector Machine

In recent years, network traffic data have become larger and more complex, leading to higher possibilities of network intrusion. Traditional intrusion detection methods face difficulty in processing high-speed network data and cannot detect currently unknown attacks. Therefore, this paper proposes a network attack detection method combining a flow calculation and deep learning. The method consists of two parts: a real-time detection algorithm based on flow calculations and frequent patterns and a classification algorithm based on the deep belief network and support vector machine(DBN-SVM). Sliding window(SW) stream data processing enables real-time detection, and the DBN-SVM algorithm can improve classification accuracy. Finally, to verify the proposed method, a system is implemented.Based on the CICIDS2017 open source data set, a series of comparative experiments are conducted. The method's real-time detection efficiency is higher than that of traditional machine learning algorithms. The attack classification accuracy is 0.7 percentage points higher than that of a DBN, which is 2 percentage points higher than that of the integrated algorithm boosting and bagging methods. Hence, it is suitable for the real-time detection of high-speed network intrusions.

Small Object Detection via Precise Region-Based Fully Convolutional Networks

In the past several years,remarkable achievements have been made in the field of object detection.Although performance is generally improving,the accuracy of small object detection remains low compared with that of large object detection.In addition,localization misalignment issues are common for small objects,as seen in GoogLeNets and residual networks(ResNets).To address this problem,we propose an improved region-based fully convolutional network(R-FCN).The presented technique improves detection accuracy and eliminates localization misalignment by replacing positionsensitive region of interest(PS-RoI)pooling with position-sensitive precise region of interest(PS-Pr-RoI)pooling,which avoids coordinate quantization and directly calculates two-order integrals for position-sensitive score maps,thus preventing a loss of spatial precision.A validation experiment was conducted in which the Microsoft common objects in context(MS COCO)training dataset was oversampled.Results showed an accuracy improvement of 3.7%for object detection tasks and an increase of 6.0%for small objects.

Lightweight authentication protocol for e-health clouds in IoT-based applications through 5G technology

Modem information technology has been utilized progressively to store and distribute a large amount of healthcare data to reduce costs and improve medical facilities.In this context,the emergence of e-Health clouds offers novel opportunities,like easy and remote accessibility of medical data.However,this achievement produces plenty of new risks and challenges like how to provide integrity,security,and confidentiality to the highly susceptible e-Health data.Among these challenges,authentication is a major issue that ensures that the susceptible medical data in clouds is not available to illegal participants.The smart card,password and biometrics are three factors of authentication which fulfill the requirement of giving high security.Numerous three-factor ECC-based authentication protocols on e-Health clouds have been presented so far.However,most of the protocols have serious security flaws and produce high computation and communication overheads.Therefore,we introduce a novel protocol for the e-Health cloud,which thwarts some major attacks,such as user anonymity,offline password guessing,impersonation,and stolen smart card attacks.Moreover,we evaluate our protocol through formal security analysis using the Random Oracle Model(ROM).The analysis shows that our proposed protocol is more efficient than many existing protocols in terms of computation and communication costs.Thus,our proposed protocol is proved to be more efficient,robust and secure.

DSAFF-Net:A Backbone Network Based on Mask R-CNN for Small Object Detection

Recently,object detection based on convolutional neural networks(CNNs)has developed rapidly.The backbone networks for basic feature extraction are an important component of the whole detection task.Therefore,we present a new feature extraction strategy in this paper,which name is DSAFF-Net.In this strategy,we design:1)a sandwich attention feature fusion module(SAFF module).Its purpose is to enhance the semantic information of shallow features and resolution of deep features,which is beneficial to small object detection after feature fusion.2)to add a new stage called D-block to alleviate the disadvantages of decreasing spatial resolution when the pooling layer increases the receptive field.The method proposed in the new stage replaces the original method of obtaining the P6 feature map and uses the result as the input of the regional proposal network(RPN).In the experimental phase,we use the new strategy to extract features.The experiment takes the public dataset of Microsoft Common Objects in Context(MS COCO)object detection and the dataset of Corona Virus Disease 2019(COVID-19)image classification as the experimental object respectively.The results show that the average recognition accuracy of COVID-19 in the classification dataset is improved to 98.163%,and small object detection in object detection tasks is improved by 4.0%.