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Flame image recognition of alumina rotary kiln by artificial neural network and support vector machine methods 被引量:18
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作者 张红亮 邹忠 +1 位作者 李劼 陈湘涛 《Journal of Central South University of Technology》 EI 2008年第1期39-43,共5页
Based on the Fourier transform, a new shape descriptor was proposed to represent the flame image. By employing the shape descriptor as the input, the flame image recognition was studied by the methods of the artificia... Based on the Fourier transform, a new shape descriptor was proposed to represent the flame image. By employing the shape descriptor as the input, the flame image recognition was studied by the methods of the artificial neural network(ANN) and the support vector machine(SVM) respectively. And the recognition experiments were carried out by using flame image data sampled from an alumina rotary kiln to evaluate their effectiveness. The results show that the two recognition methods can achieve good results, which verify the effectiveness of the shape descriptor. The highest recognition rate is 88.83% for SVM and 87.38% for ANN, which means that the performance of the SVM is better than that of the ANN. 展开更多
关键词 rotary kiln flame image image recognition shape descriptor artificial neural network support vector machine
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Artificial Neural Network to Predict Leaf Population Chlorophyll Content from Cotton Plant Images 被引量:11
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作者 SUO Xing-mei JIANG Ying-tao +3 位作者 YANG Mei LI Shao-kun WANG Ke-ru WANG Chong-tao 《Agricultural Sciences in China》 CAS CSCD 2010年第1期38-45,共8页
Leaf population chlorophyll content in a population of crops, if obtained in a timely manner, served as a key indicator for growth management and diseases diagnosis. In this paper, a three-layer multilayer perceptron ... Leaf population chlorophyll content in a population of crops, if obtained in a timely manner, served as a key indicator for growth management and diseases diagnosis. In this paper, a three-layer multilayer perceptron (MLP) artificial neural network (ANN) based prediction system was presented for predicting the leaf population chlorophyll content from the cotton plant images. As the training of this prediction system relied heavily on how well those leaf green pixels were separated from background noises in cotton plant images, a global thresholding algorithm and an omnidirectional scan noise filtering coupled with the hue histogram statistic method were designed for leaf green pixel extraction. With the obtained leaf green pixels, the system training was carried out by applying a back propagation algorithm. The proposed system was tested to predict the chlorophyll content from the cotton plant images. The results using the proposed system were in sound agreement with those obtained by the destructive method. The average prediction relative error for the chlorophyll density (μg cm^-2) in the 17 testing images was 8.41%. 展开更多
关键词 artificial neural network image processing cotton plant leaf population chlorophyll content prediction
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Determination of geological strength index of jointed rock mass based on image processing 被引量:7
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作者 Kunui Hong Eunchol Han Kwangsong Kang 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2017年第4期702-708,共7页
The geological strength index(GSI) system,widely used for the design and practice of mining process,is a unique rock mass classification system related to the rock mass strength and deformation parameters based on the... The geological strength index(GSI) system,widely used for the design and practice of mining process,is a unique rock mass classification system related to the rock mass strength and deformation parameters based on the generalized Hoek-Brown and Mohr-Coulomb failure criteria.The GSI can be estimated using standard chart and field observations of rock mass blockiness and discontinuity surface conditions.The GSI value gives a numerical representation of the overall geotechnical quality of the rock mass.In this study,we propose a method to determine the GSI quantitatively using photographic images of in situ jointed rock mass with image processing technology,fractal theory and artificial neural network(ANN).We employ the GSI system to characterize the jointed rock mass around the working in a coal mine.The relative error between the proposed value and the given value in the GSI chart is less than 3.6%. 展开更多
关键词 Jointed rock mass Geological strength index(GSI) image processing Fractal dimension artificial neural network(ANN)
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Mixed-decomposed convolutional network:A lightweight yet efficient convolutional neural network for ocular disease recognition
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作者 Xiaoqing Zhang Xiao Wu +5 位作者 Zunjie Xiao Lingxi Hu Zhongxi Qiu Qingyang Sun Risa Higashita Jiang Liu 《CAAI Transactions on Intelligence Technology》 SCIE EI 2024年第2期319-332,共14页
Eye health has become a global health concern and attracted broad attention.Over the years,researchers have proposed many state-of-the-art convolutional neural networks(CNNs)to assist ophthalmologists in diagnosing oc... Eye health has become a global health concern and attracted broad attention.Over the years,researchers have proposed many state-of-the-art convolutional neural networks(CNNs)to assist ophthalmologists in diagnosing ocular diseases efficiently and precisely.However,most existing methods were dedicated to constructing sophisticated CNNs,inevitably ignoring the trade-off between performance and model complexity.To alleviate this paradox,this paper proposes a lightweight yet efficient network architecture,mixeddecomposed convolutional network(MDNet),to recognise ocular diseases.In MDNet,we introduce a novel mixed-decomposed depthwise convolution method,which takes advantage of depthwise convolution and depthwise dilated convolution operations to capture low-resolution and high-resolution patterns by using fewer computations and fewer parameters.We conduct extensive experiments on the clinical anterior segment optical coherence tomography(AS-OCT),LAG,University of California San Diego,and CIFAR-100 datasets.The results show our MDNet achieves a better trade-off between the performance and model complexity than efficient CNNs including MobileNets and MixNets.Specifically,our MDNet outperforms MobileNets by 2.5%of accuracy by using 22%fewer parameters and 30%fewer computations on the AS-OCT dataset. 展开更多
关键词 artificial intelligence deep learning deep neural networks image analysis image classification medical applications medical image processing
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A Comprehensive Investigation of Machine Learning Feature Extraction and ClassificationMethods for Automated Diagnosis of COVID-19 Based on X-ray Images 被引量:7
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作者 Mazin Abed Mohammed Karrar Hameed Abdulkareem +6 位作者 Begonya Garcia-Zapirain Salama A.Mostafa Mashael S.Maashi Alaa S.Al-Waisy Mohammed Ahmed Subhi Ammar Awad Mutlag Dac-Nhuong Le 《Computers, Materials & Continua》 SCIE EI 2021年第3期3289-3310,共22页
The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global health.The biological structure and symptoms of COVID-19 are similar to other viral chest maladies,whi... The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global health.The biological structure and symptoms of COVID-19 are similar to other viral chest maladies,which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease.In this study,an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods(e.g.,artificial neural network(ANN),support vector machine(SVM),linear kernel and radial basis function(RBF),k-nearest neighbor(k-NN),Decision Tree(DT),andCN2 rule inducer techniques)and deep learningmodels(e.g.,MobileNets V2,ResNet50,GoogleNet,DarkNet andXception).A largeX-ray dataset has been created and developed,namely the COVID-19 vs.Normal(400 healthy cases,and 400 COVID cases).To the best of our knowledge,it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection cases.Based on the results obtained from the experiments,it can be concluded that all the models performed well,deep learning models had achieved the optimum accuracy of 98.8%in ResNet50 model.In comparison,in traditional machine learning techniques, the SVM demonstrated the best result for an accuracy of 95% and RBFaccuracy 94% for the prediction of coronavirus disease 2019. 展开更多
关键词 Coronavirus disease COVID-19 diagnosis machine learning convolutional neural networks resnet50 artificial neural network support vector machine X-ray images feature transfer learning
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Deep learning with convolutional neural networks for identification of liver masses and hepatocellular carcinoma: A systematic review 被引量:11
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作者 Samy A Azer 《World Journal of Gastrointestinal Oncology》 SCIE CAS 2019年第12期1218-1230,共13页
BACKGROUND Artificial intelligence,such as convolutional neural networks(CNNs),has been used in the interpretation of images and the diagnosis of hepatocellular cancer(HCC)and liver masses.CNN,a machine-learning algor... BACKGROUND Artificial intelligence,such as convolutional neural networks(CNNs),has been used in the interpretation of images and the diagnosis of hepatocellular cancer(HCC)and liver masses.CNN,a machine-learning algorithm similar to deep learning,has demonstrated its capability to recognise specific features that can detect pathological lesions.AIM To assess the use of CNNs in examining HCC and liver masses images in the diagnosis of cancer and evaluating the accuracy level of CNNs and their performance.METHODS The databases PubMed,EMBASE,and the Web of Science and research books were systematically searched using related keywords.Studies analysing pathological anatomy,cellular,and radiological images on HCC or liver masses using CNNs were identified according to the study protocol to detect cancer,differentiating cancer from other lesions,or staging the lesion.The data were extracted as per a predefined extraction.The accuracy level and performance of the CNNs in detecting cancer or early stages of cancer were analysed.The primary outcomes of the study were analysing the type of cancer or liver mass and identifying the type of images that showed optimum accuracy in cancer detection.RESULTS A total of 11 studies that met the selection criteria and were consistent with the aims of the study were identified.The studies demonstrated the ability to differentiate liver masses or differentiate HCC from other lesions(n=6),HCC from cirrhosis or development of new tumours(n=3),and HCC nuclei grading or segmentation(n=2).The CNNs showed satisfactory levels of accuracy.The studies aimed at detecting lesions(n=4),classification(n=5),and segmentation(n=2).Several methods were used to assess the accuracy of CNN models used.CONCLUSION The role of CNNs in analysing images and as tools in early detection of HCC or liver masses has been demonstrated in these studies.While a few limitations have been identified in these studies,overall there was an optimal level of accuracy of the CNNs used in segmentation and classification of liver cancers images. 展开更多
关键词 Deep learning Convolutional neural network HEPATOCELLULAR CARCINOMA LIVER MASSES LIVER cancer Medical imaging Classification Segmentation artificial INTELLIGENCE COMPUTER-AIDED diagnosis
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Early SkinDiseaseIdentification Using Deep Neural Network 被引量:1
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作者 Vinay Gautam Naresh Kumar Trivedi +4 位作者 Abhineet Anand Rajeev Tiwari Atef Zaguia Deepika Koundal Sachin Jain 《Computer Systems Science & Engineering》 SCIE EI 2023年第3期2259-2275,共17页
Skin lesions detection and classification is a prominent issue and difficult even for extremely skilled dermatologists and pathologists.Skin disease is the most common disorder triggered by fungus,viruses,bacteria,all... Skin lesions detection and classification is a prominent issue and difficult even for extremely skilled dermatologists and pathologists.Skin disease is the most common disorder triggered by fungus,viruses,bacteria,allergies,etc.Skin diseases are most dangerous and may be the cause of serious damage.Therefore,it requires to diagnose it at an earlier stage,but the diagnosis therapy itself is complex and needs advanced laser and photonic therapy.This advance therapy involvesfinancial burden and some other ill effects.Therefore,it must use artificial intelligence techniques to detect and diagnose it accurately at an earlier stage.Several techniques have been proposed to detect skin disease at an earlier stage but fail to get accuracy.Therefore,the primary goal of this paper is to classify,detect and provide accurate information about skin diseases.This paper deals with the same issue by proposing a high-performance Convolution neural network(CNN)to classify and detect skin disease at an earlier stage.The complete meth-odology is explained in different folds:firstly,the skin diseases images are pre-processed with processing techniques,and secondly,the important feature of the skin images are extracted.Thirdly,the pre-processed images are analyzed at different stages using a Deep Convolution Neural Network(DCNN).The approach proposed in this paper is simple,fast,and shows accurate results up to 98%and used to detect six different disease types. 展开更多
关键词 Convolution neural network(CNN) skin disease deep learning(DL) image processing artificial intelligence(AI)
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Design and implementation of gasifier flame detection system based on SCNN
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作者 WU Jin DAI Wei +1 位作者 WANG Yu ZHAO Bo 《High Technology Letters》 EI CAS 2022年第4期401-410,共10页
Flame detection is a research hotspot in industrial production,and it has been widely used in various fields.Based on the ignition and combustion video sequence,this paper aims to improve the accuracy and unintuitive ... Flame detection is a research hotspot in industrial production,and it has been widely used in various fields.Based on the ignition and combustion video sequence,this paper aims to improve the accuracy and unintuitive detection results of the current flame detection methods of gasifier and industrial boiler.A furnace flame detection model based on support vector machine convolutional neural network(SCNN)is proposed.This algorithm uses the advantages of neural networks in the field of image classification to process flame burning video sequences which needs detailed analysis.Firstly,the support vector machine(SVM)with better small sample classification effect is used to replace the Softmax classification layer of the convolutional neural network(CNN)network.Secondly,a Dropout layer is introduced to improve the generalization ability of the network.Subsequently,the area,frequency and other important parameters of the flame image are analyzed and processed.Eventually,the experimental results show that the flame detection model designed in this paper is more accurate than the CNN model,and the accuracy of the judgment on the flame data set collected in the gasifier furnace reaches 99.53%.After several ignition tests,the furnace flame of the gasifier can be detected in real time. 展开更多
关键词 support vector machine convolutional neural network(SCNN) support vector machine(SVM) flame detection flame image processing GASIFIER
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An Optimal Deep Learning Based Computer-Aided Diagnosis System for Diabetic Retinopathy
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作者 Phong Thanh Nguyen Vy Dang Bich Huynh +3 位作者 Khoa Dang Vo Phuong Thanh Phan Eunmok Yang Gyanendra Prasad Joshi 《Computers, Materials & Continua》 SCIE EI 2021年第3期2815-2830,共16页
Diabetic Retinopathy(DR)is a significant blinding disease that poses serious threat to human vision rapidly.Classification and severity grading of DR are difficult processes to accomplish.Traditionally,it depends on o... Diabetic Retinopathy(DR)is a significant blinding disease that poses serious threat to human vision rapidly.Classification and severity grading of DR are difficult processes to accomplish.Traditionally,it depends on ophthalmoscopically-visible symptoms of growing severity,which is then ranked in a stepwise scale from no retinopathy to various levels of DR severity.This paper presents an ensemble of Orthogonal Learning Particle Swarm Optimization(OPSO)algorithm-based Convolutional Neural Network(CNN)Model EOPSO-CNN in order to perform DR detection and grading.The proposed EOPSO-CNN model involves three main processes such as preprocessing,feature extraction,and classification.The proposed model initially involves preprocessing stage which removes the presence of noise in the input image.Then,the watershed algorithm is applied to segment the preprocessed images.Followed by,feature extraction takes place by leveraging EOPSO-CNN model.Finally,the extracted feature vectors are provided to a Decision Tree(DT)classifier to classify the DR images.The study experiments were carried out using Messidor DR Dataset and the results showed an extraordinary performance by the proposed method over compared methods in a considerable way.The simulation outcome offered the maximum classification with accuracy,sensitivity,and specificity values being 98.47%,96.43%,and 99.02%respectively. 展开更多
关键词 Diabetic retinopathy convolutional neural network CLASSIFICATION image processing computer-aided diagnosis
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人工智能辅助染色体核型分析技术在产前诊断中的应用研究
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作者 郭彩琴 王峻峰 +4 位作者 杨岚 石锦平 唐叶 赵頔 吴晓 《中国全科医学》 CAS 北大核心 2024年第23期2883-2887,2896,共6页
背景染色体异常是导致出生缺陷的常见原因,核型分析仍是产前诊断染色体异常的重要方法,也是出生缺陷防控的有效手段,但目前核型分析尤其是染色体图像分割分类主要依靠人工,费时费力。人工智能(AI)是核型分析的一种新方式,研究其在产前... 背景染色体异常是导致出生缺陷的常见原因,核型分析仍是产前诊断染色体异常的重要方法,也是出生缺陷防控的有效手段,但目前核型分析尤其是染色体图像分割分类主要依靠人工,费时费力。人工智能(AI)是核型分析的一种新方式,研究其在产前染色体核型诊断中的价值具有重要意义。目的探讨AI在产前染色体核型诊断中的应用效果和临床价值。方法选取2020—2022年在无锡市妇幼保健院医学遗传与产前诊断科接受介入性产前诊断、行羊水染色体核型分析的1000例孕妇。采用双线模式:一线AI阅片后,由1名遗传医师审核,二线由另1名遗传医师应用Ikaros核型分析工作站阅片,记录各自的诊断结果及所需时间。样本的最终诊断结果以一线的人工审核和二线的人工阅片结果为准。结果1000例羊水样本中,AI诊断正常核型735例、非整倍体233例、结构异常0例、嵌合体32例。AI辅助遗传医师的诊断结果与遗传医师应用Ikaros系统的诊断结果完全一致,正常核型、非整倍体、结构异常、嵌合体分别是689、233、45、33例。与AI辅助遗传医师相比,AI诊断具有强一致性(Kappa值=0.895,95%CI=0.866~0.924,P<0.01)。AI诊断准确率为95.4%,灵敏度为95.4%,阳性预测值为100.0%。其中,诊断正常核型、非整倍体、结构异常、嵌合体的灵敏度分别为100.0%、100.0%、0、97.0%;阳性预测值分别为100.0%、100.0%、0、100.0%。AI平均诊断用时少于AI辅助遗传医师和Ikaros辅助遗传医师(P<0.001);AI辅助遗传医师平均诊断用时少于Ikaros辅助遗传医师组(P<0.001)。结论AI分析羊水核型的自动化程度高,但识别染色体结构异常的能力有待提高,建议采用AI联合遗传医师阅片的方式应用于临床,以保证产前诊断的质量并提高效率。 展开更多
关键词 染色体核型分析 人工智能 产前诊断 卷积神经网络 图像分割 染色体分类
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Learning to represent 2D human face with mathematical model
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作者 Liping Zhang Weijun Li +3 位作者 Linjun Sun Lina Yu Xin Ning Xiaoli Dong 《CAAI Transactions on Intelligence Technology》 SCIE EI 2024年第1期54-68,共15页
How to represent a human face pattern?While it is presented in a continuous way in human visual system,computers often store and process it in a discrete manner with 2D arrays of pixels.The authors attempt to learn a ... How to represent a human face pattern?While it is presented in a continuous way in human visual system,computers often store and process it in a discrete manner with 2D arrays of pixels.The authors attempt to learn a continuous surface representation for face image with explicit function.First,an explicit model(EmFace)for human face representation is pro-posed in the form of a finite sum of mathematical terms,where each term is an analytic function element.Further,to estimate the unknown parameters of EmFace,a novel neural network,EmNet,is designed with an encoder-decoder structure and trained from massive face images,where the encoder is defined by a deep convolutional neural network and the decoder is an explicit mathematical expression of EmFace.The authors demonstrate that our EmFace represents face image more accurate than the comparison method,with an average mean square error of 0.000888,0.000936,0.000953 on LFW,IARPA Janus Benchmark-B,and IJB-C datasets.Visualisation results show that,EmFace has a higher representation performance on faces with various expressions,postures,and other factors.Furthermore,EmFace achieves reasonable performance on several face image processing tasks,including face image restoration,denoising,and transformation. 展开更多
关键词 artificial neural networks face analysis image processing mathematics computing
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基于形态学影像后处理技术在局灶性皮质发育不良诊治中的研究进展
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作者 王仁 王小强 +1 位作者 史雪峰 张新定 《中国神经精神疾病杂志》 CAS CSCD 北大核心 2024年第3期178-182,共5页
局灶性皮质发育不良(focal cortical dysplasia,FCD)是导致药物难治性癫痫(drug-resistant epilepsy,DRE)最常见的病因之一,其神经影像学特征是临床评估的重要组成部分。因为只有部分类型FCD在MRI表现异常,对于MRI阴性的FCD,诊断和治疗... 局灶性皮质发育不良(focal cortical dysplasia,FCD)是导致药物难治性癫痫(drug-resistant epilepsy,DRE)最常见的病因之一,其神经影像学特征是临床评估的重要组成部分。因为只有部分类型FCD在MRI表现异常,对于MRI阴性的FCD,诊断和治疗仍存在诸多困难。基于形态学的影像后处理技术发展日新月异,各种辅助诊断和治疗的影像后处理工具如Matlab、3D slicer、SinoPlan、MRIcro等越来越受到广大癫痫外科学者的青睐,不仅可将MRI显示的异常病灶进行剥离并三维重建,同时还可辅助显现出肉眼难以辨识的潜在异常部位,大大提高了FCD病灶检出率,进一步满足了临床对精准诊断和治疗FCD的需要,为难治性癫痫的诊治创造了新的思路和方法,也为临床更加全面科学的诊断和治疗提供了更多的参考。 展开更多
关键词 局灶性皮质发育不良 影像后处理技术 MRI阳性 MRI阴性 人工神经网络
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基于图像分析技术的全脂奶粉品质软测量模型构建 被引量:2
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作者 丁浩晗 谢祯奇 +2 位作者 田嘉伟 辛星 王震宇 《食品与发酵工业》 CAS CSCD 北大核心 2024年第10期273-281,共9页
针对传统奶粉品质检测方法的主观性和滞后性问题,该研究利用奶粉的加工条件和颗粒形态构建了一个基于图像分析技术和人工智能算法的奶粉品质软测量模型,用于准确、实时地预测速溶全脂奶粉的分散性和溶解性这2个重要品质性能。利用显微... 针对传统奶粉品质检测方法的主观性和滞后性问题,该研究利用奶粉的加工条件和颗粒形态构建了一个基于图像分析技术和人工智能算法的奶粉品质软测量模型,用于准确、实时地预测速溶全脂奶粉的分散性和溶解性这2个重要品质性能。利用显微数码摄像头和图像处理技术,获取了奶粉颗粒的形态参数。同时,利用重采样技术解决了奶粉工厂原始数据集中的数据不平衡问题。根据实验获取的奶粉颗粒形态参数和奶粉工厂提供的奶粉生产时的加工条件数据,选用偏最小二乘模型和人工神经网络模型构建了速溶全脂奶粉分散性和溶解性的软测量模型,并利用原始数据验证了所构建模型的准确性。结果表明,用于预测奶粉分散性和溶解性所构建的偏最小二乘模型的Q^(2)和R^(2)分别为Q^(2)=0.72,R^(2)=0.94和Q^(2)=0.85,R^(2)=0.95,而用于预测奶粉分散性和溶解性所构建的人工神经网络模型的R^(2)分别为0.97和0.96。模型的良好性能证明,这些模型可以准确、实时地预测奶粉的分散性和溶解性,并为奶粉品质的在线检测提供了新的方法。 展开更多
关键词 速溶全脂奶粉 图像处理 品质检测 偏最小二乘法 人工神经网络
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舌象图像深度特征在慢性失眠疗效评价中的应用
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作者 陈杰 王皓轩 +2 位作者 钱卓雅 王瑜 许家佗 《上海中医药杂志》 CSCD 2024年第11期86-89,共4页
目的基于舌象深度特征构建慢性失眠常见中医证型的疗效评价模型。方法基于220例健康人舌象,应用深度卷积神经网络(ResNet50)分别对241例痰热扰心证、185例心脾两虚证、266例心肾不交证慢性失眠患者舌象进行二分类学习,得到验证集超过95... 目的基于舌象深度特征构建慢性失眠常见中医证型的疗效评价模型。方法基于220例健康人舌象,应用深度卷积神经网络(ResNet50)分别对241例痰热扰心证、185例心脾两虚证、266例心肾不交证慢性失眠患者舌象进行二分类学习,得到验证集超过95%分类准确率的3个不同基准模型,将其参数固定后,再将中药治疗前后的图像分别输入该模型,获得相应的概率输出,即该病例治疗前后的健康似然度,并进行分析。结果治疗期间,慢性失眠不同证型中药治疗有效病例舌象特征健康似然度呈线性升高趋势;中药治疗无效病例的舌象特征健康似然度的变化情况与中医证型有关,分别呈线性下降(痰热扰心证)、先升后降至治疗前水平(心脾两虚证)以及缓慢上升(心肾不交证)的趋势。结论基于舌象深度特征的慢性失眠疗效评价方法及可视化呈现,具有良好的客观性、可读性。 展开更多
关键词 慢性失眠 疗效评价 舌象特征 深度神经网络 深度学习 中医诊断 人工智能
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人工神经网络在脊柱外科中的应用进展
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作者 何伟焘 陈德春(综述) 王新涛(审校) 《临床骨科杂志》 2024年第2期289-293,共5页
目前在脊柱外科领域,人工神经网络凭借着对医学图像分析和患者数据分析的出色表现被众多研究者重视,其在预测患者并发症风险、诊断患者脊柱相关疾病、开发小鼠病理模型等临床和科研方面的准确性及实用性越来越高,重要性日益显著。但是,... 目前在脊柱外科领域,人工神经网络凭借着对医学图像分析和患者数据分析的出色表现被众多研究者重视,其在预测患者并发症风险、诊断患者脊柱相关疾病、开发小鼠病理模型等临床和科研方面的准确性及实用性越来越高,重要性日益显著。但是,人工神经网络尚未被大多数脊柱外科医师充分认知。该文就人工神经网络在脊柱外科的图像分析、临床诊断及治疗等方面的研究进行综述,阐述人工神经网络在脊柱外科中的应用,并探讨其应用的局限性和未来发展方向。 展开更多
关键词 人工神经网络 脊柱外科 辅助诊断 医学图像分析
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基于深度学习的生物组织病理图像分析在海洋监测中的发展潜力及案例分析
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作者 邸雅楠 赵若轩 徐建洲 《海洋学研究》 CSCD 北大核心 2024年第3期64-74,共11页
生物组织病理指标可用于评价海洋生物健康,但在应用中存在效率低、成本高、主观性强等缺陷。将人工智能技术引入生物组织病理分析,可以发挥其高通量的图像分析优势,突破其在海洋生物健康评价和监测中的应用限制。该文通过对海洋生物组... 生物组织病理指标可用于评价海洋生物健康,但在应用中存在效率低、成本高、主观性强等缺陷。将人工智能技术引入生物组织病理分析,可以发挥其高通量的图像分析优势,突破其在海洋生物健康评价和监测中的应用限制。该文通过对海洋生物组织健康评价指标、人工智能技术的图像分析应用以及利用人工智能开展组织病理图像处理的文献调研,提出基于深度学习的海洋动物组织病理图像分析思路,并以海洋贻贝作为模式生物进行技术开发。经过对贻贝鳃组织病理影像数据的训练、验证和预测等过程,确定Res-UNet深度学习模型可对贻贝在典型环境污染物胁迫下的病理损伤进行高效、准确定量,构建了一种能够自动化、高通量和弱主观性地分析海洋贻贝组织病理影像的工作流程,为海洋生物健康评价、海洋监测提供新思路与新技术。 展开更多
关键词 人工智能 神经网络 病理图像处理 生物健康评价 海洋模式生物 海洋贻贝 组织病理定量 鳃丝面积
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An object detection approach with residual feature fusion and second-order term attention mechanism
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作者 Cuijin Li Zhong Qu Shengye Wang 《CAAI Transactions on Intelligence Technology》 SCIE EI 2024年第2期411-424,共14页
Automatically detecting and locating remote occlusion small objects from the images of complex traffic environments is a valuable and challenging research.Since the boundary box location is not sufficiently accurate a... Automatically detecting and locating remote occlusion small objects from the images of complex traffic environments is a valuable and challenging research.Since the boundary box location is not sufficiently accurate and it is difficult to distinguish overlapping and occluded objects,the authors propose a network model with a second-order term attention mechanism and occlusion loss.First,the backbone network is built on CSPDarkNet53.Then a method is designed for the feature extraction network based on an item-wise attention mechanism,which uses the filtered weighted feature vector to replace the original residual fusion and adds a second-order term to reduce the information loss in the process of fusion and accelerate the convergence of the model.Finally,an objected occlusion regression loss function is studied to reduce the problems of missed detections caused by dense objects.Sufficient experimental results demonstrate that the authors’method achieved state-of-the-art performance without reducing the detection speed.The mAP@.5 of the method is 85.8%on the Foggy_cityscapes dataset and the mAP@.5 of the method is 97.8%on the KITTI dataset. 展开更多
关键词 artificial intelligence computer vision image processing machine learning neural network object recognition
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图像处理中基于深度学习的图像语义分割综述
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作者 陈惠民 《科技资讯》 2024年第6期10-13,共4页
图像语义分割(Semantic Segmentation)是计算机视觉领域的研究热点,图像语义分割不仅能预测一幅图像中的不同类别,同时还能定位不同语义类别的位置,具有重要的研究意义和应用价值。这些方法被用于人工智能当中,应用于无人驾驶、遥感影... 图像语义分割(Semantic Segmentation)是计算机视觉领域的研究热点,图像语义分割不仅能预测一幅图像中的不同类别,同时还能定位不同语义类别的位置,具有重要的研究意义和应用价值。这些方法被用于人工智能当中,应用于无人驾驶、遥感影像检测、医疗影像等研究领域。全卷积神经网络的快速崛起推动了图像语义分割领域的发展,二者的融合取得了显著的成就。主要从语义分割的介绍出发,对近几年的代表性工作进行了阐述,并对未来的研究方向进行展望。 展开更多
关键词 图像处理 语义分割 计算机视觉 人工智能 深度神经网络
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B^(2)C^(3)NetF^(2):Breast cancer classification using an end‐to‐end deep learning feature fusion and satin bowerbird optimization controlled Newton Raphson feature selection
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作者 Mamuna Fatima Muhammad Attique Khan +2 位作者 Saima Shaheen Nouf Abdullah Almujally Shui‐Hua Wang 《CAAI Transactions on Intelligence Technology》 SCIE EI 2023年第4期1374-1390,共17页
Currently,the improvement in AI is mainly related to deep learning techniques that are employed for the classification,identification,and quantification of patterns in clinical images.The deep learning models show mor... Currently,the improvement in AI is mainly related to deep learning techniques that are employed for the classification,identification,and quantification of patterns in clinical images.The deep learning models show more remarkable performance than the traditional methods for medical image processing tasks,such as skin cancer,colorectal cancer,brain tumour,cardiac disease,Breast cancer(BrC),and a few more.The manual diagnosis of medical issues always requires an expert and is also expensive.Therefore,developing some computer diagnosis techniques based on deep learning is essential.Breast cancer is the most frequently diagnosed cancer in females with a rapidly growing percentage.It is estimated that patients with BrC will rise to 70%in the next 20 years.If diagnosed at a later stage,the survival rate of patients with BrC is shallow.Hence,early detection is essential,increasing the survival rate to 50%.A new framework for BrC classification is presented that utilises deep learning and feature optimization.The significant steps of the presented framework include(i)hybrid contrast enhancement of acquired images,(ii)data augmentation to facilitate better learning of the Convolutional Neural Network(CNN)model,(iii)a pre‐trained ResNet‐101 model is utilised and modified according to selected dataset classes,(iv)deep transfer learning based model training for feature extraction,(v)the fusion of features using the proposed highly corrected function‐controlled canonical correlation analysis approach,and(vi)optimal feature selection using the modified Satin Bowerbird Optimization controlled Newton Raphson algorithm that finally classified using 10 machine learning classifiers.The experiments of the proposed framework have been carried out using the most critical and publicly available dataset,such as CBISDDSM,and obtained the best accuracy of 94.5%along with improved computation time.The comparison depicts that the presented method surpasses the current state‐ofthe‐art approaches. 展开更多
关键词 artificial intelligence artificial neural network deep learning medical image processing multi‐objective optimization
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基于炉内火焰图像的燃烧诊断 被引量:30
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作者 卫成业 严建华 +4 位作者 商敏儿 马增益 王飞 王新军 岑可法 《动力工程》 CSCD 北大核心 2003年第3期2420-2427,2419,共9页
讨论了基于炉内辐射图像的燃烧诊断方法 ,并根据采集的单角燃烧器火焰图像 ,讨论了特征值的意义和提取方法 ;设计并训练了 Kohonen自组织神经网络和 BP神经网络分别实现燃烧状态判断和预测等燃烧诊断功能。图 8参 1
关键词 图像处理 人工神经网络 燃烧诊断
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