A Survey of Convolutional Neural Network in Breast Cancer

Problems:For people all over the world,cancer is one of the most feared diseases.Cancer is one of the major obstacles to improving life expectancy in countries around the world and one of the biggest causes of death before the age of 70 in 112 countries.Among all kinds of cancers,breast cancer is the most common cancer for women.The data showed that female breast cancer had become one of themost common cancers.Aims:A large number of clinical trials have proved that if breast cancer is diagnosed at an early stage,it could give patients more treatment options and improve the treatment effect and survival ability.Based on this situation,there are many diagnostic methods for breast cancer,such as computer-aided diagnosis(CAD).Methods:We complete a comprehensive review of the diagnosis of breast cancer based on the convolutional neural network(CNN)after reviewing a sea of recent papers.Firstly,we introduce several different imaging modalities.The structure of CNN is given in the second part.After that,we introduce some public breast cancer data sets.Then,we divide the diagnosis of breast cancer into three different tasks:1.classification;2.detection;3.segmentation.Conclusion:Although this diagnosis with CNN has achieved great success,there are still some limitations.(i)There are too few good data sets.A good public breast cancer dataset needs to involve many aspects,such as professional medical knowledge,privacy issues,financial issues,dataset size,and so on.(ii)When the data set is too large,the CNN-based model needs a sea of computation and time to complete the diagnosis.(iii)It is easy to cause overfitting when using small data sets.

RBEBT: A ResNet-Based BA-ELM for Brain Tumor Classification

Brain tumor refers to the formation of abnormal cells in the brain.It can be divided into benign and malignant.The main diagnostic methods for brain tumors are plain X-ray film,Magnetic resonance imaging(MRI),and so on.However,these artificial diagnosis methods are easily affected by external factors.Scholars have made such impressive progress in brain tumors classification by using convolutional neural network(CNN).However,there are still some problems:(i)There are many parameters in CNN,which require much calculation.(ii)The brain tumor data sets are relatively small,which may lead to the overfitting problem in CNN.In this paper,our team proposes a novel model(RBEBT)for the automatic classification of brain tumors.We use fine-tuned ResNet18 to extract the features of brain tumor images.The RBEBT is different from the traditional CNN models in that the randomized neural network(RNN)is selected as the classifier.Meanwhile,our team selects the bat algorithm(BA)to opti7mize the parameters of RNN.We use fivefold cross-validation to verify the superiority of the RBEBT.The accuracy(ACC),specificity(SPE),precision(PRE),sensitivity(SEN),and F1-score(F1)are 99.00%,95.00%,99.00%,100.00%,and 100.00%.The classification performance of the RBEBT is greater than 95%,which can prove that the RBEBT is an effective model to classify brain tumors.

Deep-Net:Fine-Tuned Deep Neural Network Multi-Features Fusion for Brain Tumor Recognition

Manual diagnosis of brain tumors usingmagnetic resonance images(MRI)is a hectic process and time-consuming.Also,it always requires an expert person for the diagnosis.Therefore,many computer-controlled methods for diagnosing and classifying brain tumors have been introduced in the literature.This paper proposes a novel multimodal brain tumor classification framework based on two-way deep learning feature extraction and a hybrid feature optimization algorithm.NasNet-Mobile,a pre-trained deep learning model,has been fine-tuned and twoway trained on original and enhancedMRI images.The haze-convolutional neural network(haze-CNN)approach is developed and employed on the original images for contrast enhancement.Next,transfer learning(TL)is utilized for training two-way fine-tuned models and extracting feature vectors from the global average pooling layer.Then,using a multiset canonical correlation analysis(CCA)method,features of both deep learning models are fused into a single feature matrix—this technique aims to enhance the information in terms of features for better classification.Although the information was increased,computational time also jumped.This issue is resolved using a hybrid feature optimization algorithm that chooses the best classification features.The experiments were done on two publicly available datasets—BraTs2018 and BraTs2019—and yielded accuracy rates of 94.8%and 95.7%,respectively.The proposedmethod is comparedwith several recent studies andoutperformed inaccuracy.In addition,we analyze the performance of each middle step of the proposed approach and find the selection technique strengthens the proposed framework.

PSTCNN: Explainable COVID-19 diagnosis using PSO-guided self-tuning CNN

Since 2019,the coronavirus disease-19(COVID-19)has been spreading rapidly worldwide,posing an unignorable threat to the global economy and human health.It is a disease caused by severe acute respiratory syndrome coronavirus 2,a single-stranded RNA virus of the genus Betacoronavirus.This virus is highly infectious and relies on its angiotensin-converting enzyme 2-receptor to enter cells.With the increase in the number of confirmed COVID-19 diagnoses,the difficulty of diagnosis due to the lack of global healthcare resources becomes increasingly apparent.Deep learning-based computer-aided diagnosis models with high generalisability can effectively alleviate this pressure.Hyperparameter tuning is essential in training such models and significantly impacts their final performance and training speed.However,traditional hyperparameter tuning methods are usually time-consuming and unstable.To solve this issue,we introduce Particle Swarm Optimisation to build a PSO-guided Self-Tuning Convolution Neural Network(PSTCNN),allowing the model to tune hyperparameters automatically.Therefore,the proposed approach can reduce human involvement.Also,the optimisation algorithm can select the combination of hyperparameters in a targeted manner,thus stably achieving a solution closer to the global optimum.Experimentally,the PSTCNN can obtain quite excellent results,with a sensitivity of 93.65%±1.86%,a specificity of 94.32%±2.07%,a precision of 94.30%±2.04%,an accuracy of 93.99%±1.78%,an F1-score of 93.97%±1.78%,Matthews Correlation Coefficient of 87.99%±3.56%,and Fowlkes-Mallows Index of 93.97%±1.78%.Our experiments demonstrate that compared to traditional methods,hyperparameter tuning of the model using an optimisation algorithm is faster and more effective.

WACPN:A Neural Network for Pneumonia Diagnosis

Community-acquired pneumonia(CAP)is considered a sort of pneumonia developed outside hospitals and clinics.To diagnose community-acquired pneumonia(CAP)more efficiently,we proposed a novel neural network model.We introduce the 2-dimensional wavelet entropy(2d-WE)layer and an adaptive chaotic particle swarm optimization(ACP)algorithm to train the feed-forward neural network.The ACP uses adaptive inertia weight factor(AIWF)and Rossler attractor(RA)to improve the performance of standard particle swarm optimization.The final combined model is named WE-layer ACP-based network(WACPN),which attains a sensitivity of 91.87±1.37%,a specificity of 90.70±1.19%,a precision of 91.01±1.12%,an accuracy of 91.29±1.09%,F1 score of 91.43±1.09%,an MCC of 82.59±2.19%,and an FMI of 91.44±1.09%.The AUC of this WACPN model is 0.9577.We find that the maximum deposition level chosen as four can obtain the best result.Experiments demonstrate the effectiveness of both AIWF and RA.Finally,this proposed WACPN is efficient in diagnosing CAP and superior to six state-of-the-art models.Our model will be distributed to the cloud computing environment.

骨科大手术后静脉血栓栓塞症的危险因素研究

目的探讨骨科大手术患者术后静脉血栓栓塞症(VTE)的危险因素。方法选取2016年1月-2017年12月于山东中医药大学附属医院行骨科大手术的患者220例。其中术后3个月内并发VTE的102例患者作为观察组,选取同期未发生VTE的118例患者作为对照组。记录患者的各种危险因素,并采用Caprini血栓风险评估模型进行评分,分析危险因素与VTE之间的关系。结果两组患者的年龄、既往病史、纤维蛋白原、D-二聚体、同型半胱氨酸、手术体位、手术时间、输血史及输血量比较,差异有统计学意义(P<0.05);观察组Caprini血栓风险因素评分高于对照组(P<0.05);经多因素Logistic回归分析显示,既往病史、纤维蛋白原、D-二聚体、手术体位和手术时间是骨科大手术患者易栓的独立危险因素(P<0.05)。结论骨科大手术患者术后发生VTE是多种危险因素共同作用的结果,Caprini评分可较好地评估骨科大手术患者VTE发病的危险程度,而患者的既往病史、纤维蛋白原、D-二聚体、手术体位和手术时间是骨科大手术患者易栓的独立危险因素,应给予充分重视。

Deep Rank-Based Average Pooling Network for Covid-19 Recognition

(Aim)To make a more accurate and precise COVID-19 diagnosis system,this study proposed a novel deep rank-based average pooling network(DRAPNet)model,i.e.,deep rank-based average pooling network,for COVID-19 recognition.(Methods)521 subjects yield 1164 slice images via the slice level selection method.All the 1164 slice images comprise four categories:COVID-19 positive;community-acquired pneumonia;second pulmonary tuberculosis;and healthy control.Our method firstly introduced an improved multiple-way data augmentation.Secondly,an n-conv rankbased average pooling module(NRAPM)was proposed in which rank-based pooling—particularly,rank-based average pooling(RAP)—was employed to avoid overfitting.Third,a novel DRAPNet was proposed based on NRAPM and inspired by the VGGnetwork.Grad-CAM was used to generate heatmaps and gave our AI model an explainable analysis.(Results)Our DRAPNet achieved a micro-averaged F1 score of 95.49%by 10 runs over the test set.The sensitivities of the four classes were 95.44%,96.07%,94.41%,and 96.07%,respectively.The precisions of four classes were 96.45%,95.22%,95.05%,and 95.28%,respectively.The F1 scores of the four classes were 95.94%,95.64%,94.73%,and 95.67%,respectively.Besides,the confusion matrix was given.(Conclusions)The DRAPNet is effective in diagnosing COVID-19 and other chest infectious diseases.The RAP gives better results than four other methods:strided convolution,l2-norm pooling,average pooling,and max pooling.

VISPNN:VGG-Inspired Stochastic Pooling Neural Network

Aim Alcoholism is a disease that a patient becomes dependent or addicted to alcohol.This paper aims to design a novel artificial intelligence model that can recognize alcoholism more accurately.Methods We propose the VGG-Inspired stochastic pooling neural network(VISPNN)model based on three components:(i)a VGG-inspired mainstay network,(ii)the stochastic pooling technique,which aims to outperform traditional max pooling and average pooling,and(iii)an improved 20-way data augmentation(Gaussian noise,salt-and-pepper noise,speckle noise,Poisson noise,horizontal shear,vertical shear,rotation,Gamma correction,random translation,and scaling on both raw image and its horizontally mirrored image).In addition,two networks(Net-I and Net-II)are proposed in ablation studies.Net-I is based on VISPNN by replacing stochastic pooling with ordinary max pooling.Net-II removes the 20-way data augmentation.Results The results by ten runs of 10-fold cross-validation show that our VISPNN model gains a sensitivity of 97.98±1.32,a specificity of 97.80±1.35,a precision of 97.78±1.35,an accuracy of 97.89±1.11,an F1 score of 97.87±1.12,an MCC of 95.79±2.22,an FMI of 97.88±1.12,and an AUC of 0.9849,respectively.Conclusion The performance of our VISPNN model is better than two internal networks(Net-I and Net-II)and ten state-of-the-art alcoholism recognition methods.

First light for the sodium laser guide star adaptive optics system on the Lijiang 1.8 m telescope

A first generation sodium Laser Guide Star Adaptive Optics System （LGS-AOS） was developed and integrated into the Lijiang 1.8 m telescope in 2013. The LGS-AOS has three sub-systems： （1） a 20W long pulsed sodium laser, （2） a 300-millimeter-diameter laser launch telescope, and （3） a 37-element com- pact adaptive optics system. On 2014 January 25, we obtained high resolution images of an my 8.18 star, HIP 43963, during the first light of the LGS-AOS. In this paper, the sodium laser, the laser launch telescope, the compact adaptive optics system and the first light results will be presented.

Optimized Ensemble Algorithm for Predicting Metamaterial Antenna Parameters

Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve performance.Metamaterial antennas can overcome the bandwidth constraint associated with tiny antennas.Machine learning is receiving a lot of interest in optimizing solutions in a variety of areas.Machine learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today’s technology.The accuracy of the forecast is mostly determined by the model used.The purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial Antenna.Support Vector Machines(SVM),Random Forest,K-Neighbors Regressor,and Decision Tree Regressor were utilized as the basic models.The Adaptive Dynamic Polar Rose Guided Whale Optimization method,named AD-PRS-Guided WOA,was used to pick the optimal features from the datasets.The suggested model is compared to models based on five variables and to the average ensemble model.The findings indicate that the presented model using Random Forest results in a Root Mean Squared Error(RMSE)of(0.0102)for bandwidth and RMSE of(0.0891)for gain.This is superior to other models and can accurately predict antenna bandwidth and gain.

Pseudo Zernike Moment and Deep Stacked Sparse Autoencoder for COVID-19 Diagnosis

(Aim)COVID-19 is an ongoing infectious disease.It has caused more than 107.45 m confirmed cases and 2.35 m deaths till 11/Feb/2021.Traditional computer vision methods have achieved promising results on the automatic smart diagnosis.(Method)This study aims to propose a novel deep learning method that can obtain better performance.We use the pseudo-Zernike moment(PZM),derived from Zernike moment,as the extracted features.Two settings are introducing:(i)image plane over unit circle;and(ii)image plane inside the unit circle.Afterward,we use a deep-stacked sparse autoencoder(DSSAE)as the classifier.Besides,multiple-way data augmentation is chosen to overcome overfitting.The multiple-way data augmentation is based on Gaussian noise,salt-and-pepper noise,speckle noise,horizontal and vertical shear,rotation,Gamma correction,random translation and scaling.(Results)10 runs of 10-fold cross validation shows that our PZM-DSSAE method achieves a sensitivity of 92.06%±1.54%,a specificity of 92.56%±1.06%,a precision of 92.53%±1.03%,and an accuracy of 92.31%±1.08%.Its F1 score,MCC,and FMI arrive at 92.29%±1.10%,84.64%±2.15%,and 92.29%±1.10%,respectively.The AUC of our model is 0.9576.(Conclusion)We demonstrate“image plane over unit circle”can get better results than“image plane inside a unit circle.”Besides,this proposed PZM-DSSAE model is better than eight state-of-the-art approaches.

Classification of Positive COVID-19 CT Scans Using Deep Learning

In medical imaging,computer vision researchers are faced with a variety of features for verifying the authenticity of classifiers for an accurate diagnosis.In response to the coronavirus 2019(COVID-19)pandemic,new testing procedures,medical treatments,and vaccines are being developed rapidly.One potential diagnostic tool is a reverse-transcription polymerase chain reaction(RT-PCR).RT-PCR,typically a time-consuming process,was less sensitive to COVID-19 recognition in the disease’s early stages.Here we introduce an optimized deep learning(DL)scheme to distinguish COVID-19-infected patients from normal patients according to computed tomography(CT)scans.In the proposed method,contrast enhancement is used to improve the quality of the original images.A pretrained DenseNet-201 DL model is then trained using transfer learning.Two fully connected layers and an average pool are used for feature extraction.The extracted deep features are then optimized with a Firefly algorithm to select the most optimal learning features.Fusing the selected features is important to improving the accuracy of the approach;however,it directly affects the computational cost of the technique.In the proposed method,a new parallel high index technique is used to fuse two optimal vectors;the outcome is then passed on to an extreme learning machine for final classification.Experiments were conducted on a collected database of patients using a 70:30 training:Testing ratio.Our results indicated an average classification accuracy of 94.76%with the proposed approach.A comparison of the outcomes to several other DL models demonstrated the effectiveness of our DL method for classifying COVID-19 based on CT scans.

Malaria Blood Smear Classification Using Deep Learning and Best Features Selection

Malaria is a critical health condition that affects both sultry and frigid region worldwide,giving rise to millions of cases of disease and thousands of deaths over the years.Malaria is caused by parasites that enter the human red blood cells,grow there,and damage them over time.Therefore,it is diagnosed by a detailed examination of blood cells under the microscope.This is the most extensively used malaria diagnosis technique,but it yields limited and unreliable results due to the manual human involvement.In this work,an automated malaria blood smear classification model is proposed,which takes images of both infected and healthy cells and preprocesses themin the L^(*)a^(*)b^(*)color space by employing several contrast enhancement methods.Feature extraction is performed using two pretrained deep convolutional neural networks,DarkNet-53 and DenseNet-201.The features are subsequently agglutinated to be optimized through a nature-based feature reduction method called the whale optimization algorithm.Several classifiers are effectuated on the reduced features,and the achieved results excel in both accuracy and time compared to previously proposed methods.

Multiclass Stomach Diseases Classication Using Deep Learning Features Optimization

In the area of medical image processing,stomach cancer is one of the most important cancers which need to be diagnose at the early stage.In this paper,an optimized deep learning method is presented for multiple stomach disease classication.The proposed method work in few important steps—preprocessing using the fusion of ltering images along with Ant Colony Optimization(ACO),deep transfer learning-based features extraction,optimization of deep extracted features using nature-inspired algorithms,and nally fusion of optimal vectors and classication using Multi-Layered Perceptron Neural Network(MLNN).In the feature extraction step,pretrained Inception V3 is utilized and retrained on selected stomach infection classes using the deep transfer learning step.Later on,the activation function is applied to Global Average Pool(GAP)for feature extraction.However,the extracted features are optimized through two different nature-inspired algorithms—Particle Swarm Optimization(PSO)with dynamic tness function and Crow Search Algorithm(CSA).Hence,both methods’output is fused by a maximal value approach and classied the fused feature vector by MLNN.Two datasets are used to evaluate the proposed method—CUI WahStomach Diseases and Combined dataset and achieved an average accuracy of 99.5%.The comparison with existing techniques,it is shown that the proposed method shows signicant performance.

A Survey on Machine Learning in COVID-19 Diagnosis

Since Corona Virus Disease 2019 outbreak,many expert groups worldwide have studied the problem and proposed many diagnostic methods.This paper focuses on the research of Corona Virus Disease 2019 diagnosis.First,the procedure of the diagnosis based on machine learning is introduced in detail,which includes medical data collection,image preprocessing,feature extraction,and image classification.Then,we review seven methods in detail:transfer learning,ensemble learning,unsupervised learning and semi-supervised learning,convolutional neural networks,graph neural networks,explainable deep neural networks,and so on.What’smore,the advantages and limitations of different diagnosis methods are compared.Although the great achievements in medical images classification in recent years,Corona Virus Disease 2019 images classification based on machine learning still encountered many problems.For example,the highly unbalanced dataset,the difficulty of collecting labeled data,and thepoorqualityof thedata.Aiming at theseproblems,wepropose some solutions andprovide a comprehensive presentation for future research.

Adaptive Object Tracking Discriminate Model for Multi-Camera Panorama Surveillance in Airport Apron

Autonomous intelligence plays a significant role in aviation security.Since most aviation accidents occur in the take-off and landing stage,accurate tracking of moving object in airport apron will be a vital approach to ensure the operation of the aircraft safely.In this study,an adaptive object tracking method based on a discriminant is proposed in multi-camera panorama surveillance of large-scale airport apron.Firstly,based on channels of color histogram,the pre-estimated object probability map is employed to reduce searching computation,and the optimization of the disturbance suppression options can make good resistance to similar areas around the object.Then the object score of probability map is obtained by the sliding window,and the candidate window with the highest probability map score is selected as the new object center.Thirdly,according to the new object location,the probability map is updated,the scale estimation function is adjusted to the size of real object.From qualitative and quantitative analysis,the comparison experiments are verified in representative video sequences,and our approach outperforms typical methods,such as distraction-aware online tracking,mean shift,variance ratio,and adaptive colour attributes.

The Research of Automatic Classification of Ultrasound Thyroid Nodules

This paper proposes a computer-aided diagnosis system which can automatically detect thyroid nodules (TNs)and discriminate them as benign or malignant. The system firstly uses variational level set active contour withgradients and phase information to complete automatic extraction of the boundaries of thyroid nodules images.Then according to thyroid ultrasound images and clinical diagnostic criteria, a new feature extraction methodbased on the fusion of shape, gray and texture is explored. Due to the imbalance of thyroid sample classes, thispaper introduces a weight factor to improve support vector machine, offering different classes of samples withdifferent weights. Finally, thyroid nodules are classified and discriminated by the improved support vector machine.Experiments show that the efficiency of discrimination on benign and malignant thyroid nodules is improved.

Classification of Domestic Refuse in Medical Institutions Based on Transfer Learning and Convolutional Neural Network

The problem of domestic refuse is becoming more and more serious with the use of all kinds of equipment in medical institutions.This matter arouses people’s attention.Traditional artificial waste classification is subjective and cannot be put accurately;moreover,the working environment of sorting is poor and the efficiency is low.Therefore,automated and effective sorting is needed.In view of the current development of deep learning,it can provide a good auxiliary role for classification and realize automatic classification.In this paper,the ResNet-50 convolutional neural network based on the transfer learning method is applied to design the image classifier to obtain the domestic refuse classification with high accuracy.By comparing the method designed in this paper with back propagation neural network and convolutional neural network,it is concluded that the CNN based on transfer learning method applied in this paper with higher accuracy rate and lower false detection rate.Further,under the shortage situation of data samples,the method with transfer learning and ResNet-50 training model is effective to improve the accuracy of image classification.

Introduction to the Special Issue on Recent Advances on Deep Learning for Medical Signal Analysis

Over the past years,deep learning has established itself as a powerful tool across a broad spectrum of domains,e.g.,prediction,classification,detection,segmentation,diagnosis,interpretation,reconstruction,etc.While deep neural networks initially found nurture in the computer vision community,they have quickly spread over medical imaging applications.

单核细胞趋化蛋白-1、高迁移率族蛋白-1联合外周血铁蛋白在诊断和评估药物性肝损伤中的价值

背景早期准确诊断、评估药物性肝损伤(drug-induced liver injury,DILI)病情对临床治疗具有重要指导意义.DILI机制复杂,涉及多种物质,铁蛋白(serum ferritin,SF)、单核细胞趋化蛋白-1(monocyte chemotactic protein-1,MCP-1)、高迁移率族蛋白-1(highmobilitygroup protein-1,HMGB1)在肝损伤时明显升高,可能与DILI有关.目的探讨外周血SF、MCP-1、HMGB1诊断DILI的价值.方法选取2018-01/2021-02我院收治的184例DILI患者,其中94例轻度患者(轻度组)、52例中度患者(中度组)、38例重度患者(重度组),并选取同期酒精性肝病、非酒精性脂肪肝病患者70例作为非DILI肝病组以及体检中心70例健康人群作为对照组,比较各组SF、MCP-1、HMGB1水平,应用皮尔森(Pearson)分析SF、MCP-1、HMGB1与谷丙转氨酶(alanine aminotransferase,ALT)、谷草转氨酶(aspartate aminotransferase,AST)、总胆红素(totalbilirubin,TBIL)、碱性磷酸酶(alkalinephosphatase,ALP)关系,斯皮尔曼(Spearman)分析SF、MCP-1、HMGB1与DILI严重程度关系,采用多分类Logistic回归方程分析DILI严重程度的相关影响因素,应用受试者工作特征曲线(receiver operating characteristic,ROC)及ROC下面积(area under the curve,AUC)分析各指标及联合诊断DILI的价值.结果ALT、AST、TBIL、ALP:重度组>中度组>轻度组>对照组(P<0.05);SF、MCP-1、HMGB1:重度组>中度组>轻度组>对照组,两两比较差异均有统计学意义(P<0.05);SF、MCP-1、HMGB1高水平者与低水平者DILI严重程度分布比较,差异有统计学意义(P<0.05);SF、MCP-1、HMGB1与ALT、AST、TBIL、ALP呈正相关(P<0.05);SF(r=0.665,P<0.001)、MCP-1(r=0.693,P<0.001)、HMGB1(r=0.728,P<0.001)与DILI严重程度正相关;多分类Logistic回归方程分析显示,将ALT、AST、TBIL、ALP控制后,SF、MCP-1、HMGB1仍是DILI病情程度的相关影响因素(P<0.05);SF、MCP-1、HMGB1诊断DILI的AUC分别为0.870、0.803、0.859,SF、MCP-1联合HMGB1诊断DILI的AUC为0.926;SF、MCP-1、HMGB1及联合鉴别重度DILI的AUC分别为0.888、0.774、0.807、0.948.结论外周血SF、MCP-1、HMGB1与DILI及其严重程度有关,在DILI诊断及病情评估呈现出较高应用价值,可作为诊断和评估DILI病情程度的生物标志物,为临床提供参考信息.