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deep transfer learning Models for Mobile-Based Ocular Disorder Identification on Retinal Images

Computers, Materials & Continua 2024年第7期80卷 139-161页

作者： Roseline Oluwaseun Ogundokun Joseph Bamidele Awotunde Hakeem Babalola Akande Cheng-Chi Lee Agbotiname Lucky Imoize Department of Multimedia Engineering Kaunas University of TechnologyKaunas44249Lithuania Department of Computer Science Landmark UniversityOmu Aran251103Nigeria Department of Computer Science Faculty of Information and Communication SciencesUniversity of IlorinIlorin240003Nigeria Department of Telecommunication Science University of IlorinIlorin230003Nigeria Department of Library and Information Science Fu Jen Catholic UniversityNew Taipei City24205TaiwanChina Department of Computer Science and Information Engineering Fintech and Blockchain Research CenterAsia UniversityTaichung City41354TaiwanChina Department of Electrical and Electronics Engineering Faculty of EngineeringUniversity of LagosAkokaLagos100213Nigeria Department of Electrical Engineering and Information Technology Institute of Digital CommunicationRuhr UniversityBochum44801Germany

Mobile technology is developing *** phone technologies have been integrated into the healthcare industry to help medical ***,computer vision models focus on image detection and classification ***2 is a computer vision model that performs well on mobile devices,but it requires cloud services to process biometric image information and provide predictions to *** leads to increased *** biometrics image datasets on mobile devices will make the prediction faster,but mobiles are resource-restricted devices in terms of storage,power,and computational ***,a model that is small in size,efficient,and has good prediction quality for biometrics image classification problems is *** pre-trained CNN(PCNN)MobileNetV2 architecture combined with a Support Vector Machine(SVM)compacts the model representation and reduces the computational cost and memory *** proposed novel approach combines quantized pre-trained CNN(PCNN)MobileNetV2 architecture with a Support Vector Machine(SVM)to represent models efficiently with low computational cost and *** contributions include evaluating three CNN models for ocular disease identification in transfer learning and deep feature plus SVM approaches,showing the superiority of deep features from MobileNetV2 and SVM classification models,comparing traditional methods,exploring six ocular diseases and normal classification with 20,111 images postdata augmentation,and reducing the number of trainable *** model is trained on ocular disorder retinal fundus image datasets according to the severity of six age-related macular degeneration(AMD),one of the most common eye illnesses,Cataract,Diabetes,Glaucoma,Hypertension,andMyopia with one class *** the experiment outcomes,it is observed that the suggested MobileNetV2-SVM model size is *** testing accuracy for MobileNetV2-SVM,InceptionV3,and MobileNetV2 is 90.11%,86.88%,a

关键词： Retinal images ocular disorder deep transfer learning disease identification mobile device

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Multiconditional machining process quality prediction using deep transfer learning network

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Advances in Manufacturing 2023年第2期11卷 329-341页

作者： Bo-Hao Li Li-Ping Zhao Yi-Yong Yao State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong UniversityXi'an710049People's Republic of China School of Mechanical Engineering Xi'an Jiaotong UniversityXi'an710049People's Republic of China

The quality prediction of machining processes is essential for maintaining process stability and improving component quality. The prediction accuracy of conventional methods relies on a significant amount of process signals under the same operating conditions. However, obtaining sufficient data during the machining process is difficult under most operating conditions, and conventional prediction methods require a certain amount of training data. Herein, a new multiconditional machining quality prediction model based on a deep transfer learning network is proposed. A process quality prediction model is built under multiple operating conditions. A deep convolutional neural network (CNN) is used to investigate the connections between multidimensional process signals and quality under source operating conditions. Three strategies, namely structure transfer, parameter transfer, and weight transfer, are used to transfer the trained CNN network to the target operating conditions. The machining quality prediction model predicts the machining quality of the target operating conditions using limited data. A multiconditional forging process is designed to validate the effectiveness of the proposed method. Compared with other data-driven methods, the proposed deep transfer learning network offers enhanced performance in terms of prediction accuracy under different conditions.

关键词： Multiconditional machining process Intelligent manufacturing deep transfer learning Quality prediction Process stability

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Spectrum Prediction Based on GAN and deep transfer learning:A Cross-Band Data Augmentation Framework

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China Communications 2021年第1期18卷 18-32页

作者： Fandi Lin Jin Chen Guoru Ding Yutao Jiao Jiachen Sun Haichao Wang College of Communications Engineering Army Engineering UniversityNanjing 210007China

This paper investigates the problem of data scarcity in spectrum prediction.A cognitive radio equipment may frequently switch the target frequency as the electromagnetic environment *** previously trained model for prediction often cannot maintain a good performance when facing small amount of historical data of the new target ***,the cognitive radio equipment usually implements the dynamic spectrum access in real time which means the time to recollect the data of the new task frequency band and retrain the model is very *** address the above issues,we develop a crossband data augmentation framework for spectrum prediction by leveraging the recent advances of generative adversarial network(GAN)and deep transfer ***,through the similarity measurement,we pre-train a GAN model using the historical data of the frequency band that is the most similar to the target frequency ***,through the data augmentation by feeding the small amount of the target data into the pre-trained GAN,temporal-spectral residual network is further trained using deep transfer learning and the generated data with high similarity from ***,experiment results demonstrate the effectiveness of the proposed framework.

关键词： cognitive radio cross-band spectrum prediction deep transfer learning generative adversarial network cross-band data augmentation framework

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Cross-Band Spectrum Prediction Based on deep transfer learning

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China Communications 2020年第2期17卷 66-80页

作者： Fandi Lin Jin Chen Jiachen Sun Guoru Ding Ling Yu College of Communications Engineering Army Engineering University

Spectrum prediction is a promising technology to infer future spectrum state by exploiting inherent patterns of historical spectrum *** practice,for a given spectrum band of interest,when facing relatively scarce historical data,spectrum prediction based on traditional learning methods does not work ***,this paper proposes a cross-band spectrum prediction model based on transfer ***,by analysing service activities and computing the distances between various frequency points based on Dynamic Time Warping,the similarity between spectrum bands has been ***,the features,which mainly affect the performance of transfer learning in the crossband spectrum prediction,are explored by leveraging transfer component ***,the effectiveness of transfer learning for the cross-band spectrum prediction has been ***,experimental results with real-world spectrum data demonstrate that the performance of the proposed model is better than the state-of-theart models when the historical spectrum data is limited.

关键词： cross-band spectrum prediction deep transfer learning long short-term memory dynamic time warping transfer component analysis

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Optimal deep transfer learning Based Colorectal Cancer Detection and Classification Model

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Computers, Materials & Continua 2023年第2期74卷 3279-3295页

作者： Mahmoud Ragab Maged Mostafa Mahmoud Amer H.Asseri Hani Choudhry Haitham A.Yacoub Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Center for Artificial Intelligence in Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Cancer Biology Unit King Fahd Medical Research CenterKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Medical Laboratory Sciences Faculty of Applied Medical SciencesKing Abdulaziz UniversityJeddah22252Saudi Arabia Department of Molecular Genetics and Enzymology Human Genetics and Genome Research InstituteNational Research CentreCairo12622Egypt Biochemistry Department Faculty of ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia Cell Biology Department Biotechnology Research InstituteNational Research CentreGiza12622Egypt

Colorectal carcinoma(CRC)is one such dispersed cancer globally and also prominent one in causing cancer-based ***,pathologists execute CRC diagnosis through visible scrutinizing under the microscope the resected tissue samples,stained and fixed through Haematoxylin and Eosin(H&E).The advancement of graphical processing systems has resulted in high potentiality for deep learning(DL)techniques in interpretating visual anatomy from high resolution medical *** study develops a slime mould algorithm with deep transfer learning enabled colorectal cancer detection and classification(SMADTL-CCDC)*** presented SMADTL-CCDC technique intends to appropriately recognize the occurrence of colorectal *** accomplish this,the SMADTLCCDC model initially undergoes pre-processing to improve the input image *** addition,a dense-EfficientNet technique was employed to extract feature vectors from the pre-processed ***,SMA with Discrete Hopfield neural network(DHNN)method was applied for the recognition and classification of colorectal *** utilization of SMA assists in appropriately selecting the parameters involved in the DHNN approach.A wide range of experiments was implemented on benchmark datasets to assess the classification performance.A comprehensive comparative study highlighted the better performance of the SMADTL-CDC model over the recent approaches.

关键词： Colorectal cancer deep transfer learning slime mould algorithm hyperparameter optimization biomedical imaging

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Classification of Citrus Plant Diseases Using deep transfer learning

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Computers, Materials & Continua 2022年第1期70卷 1401-1417页

作者： Muhammad Zia Ur Rehman Fawad Ahmed Muhammad Attique Khan Usman Tariq Sajjad Shaukat Jamal Jawad Ahmad Iqtadar Hussain Department of Electrical Engineering HITEC UniversityTaxilaPakistan Department of Computer Science HITEC UniversityTaxilaPakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Mathematics College of ScienceKing Khalid UniversityAbhaSaudi Arabia School of Computing Edinburgh Napier UniversityUK Department of Mathematics Statisticsand PhysicsQatar UniversityDoha2713Qatar

In recent years,the field of deep learning has played an important role towards automatic detection and classification of diseases in vegetables and *** in turn has helped in improving the quality and production of vegetables and *** fruits arewell known for their taste and nutritional *** are one of the natural and well known sources of vitamin C and planted *** are several diseases which severely affect the quality and yield of citrus *** this paper,a new deep learning based technique is proposed for citrus disease *** different pre-trained deep learning models have been used in this *** increase the size of the citrus dataset used in this paper,image augmentation techniques are ***,to improve the visual quality of images,hybrid contrast stretching has been *** addition,transfer learning is used to retrain the pre-trainedmodels and the feature set is enriched by using feature *** fused feature set is optimized using a meta-heuristic algorithm,the Whale Optimization Algorithm(WOA).The selected features are used for the classification of six different diseases of citrus *** proposed technique attains a classification accuracy of 95.7%with superior results when compared with recent techniques.

关键词： Citrus plant disease classification deep learning feature fusion deep transfer learning

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DTLM-DBP:deep transfer learning Models for DNA Binding Proteins Identification

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Computers, Materials & Continua 2021年第9期68卷 3563-3576页

作者： Sara Saber Uswah Khairuddin Rubiyah Yusof Ahmed Madani Department of Computer Engineering Faculty of EngineeringArab Academy of Science and TechnologyEgypt Centre for Artificial Intelligence&Robotics Malaysia-Japan International Institute of TechnologyUniversiti Teknologi Malaysia

The identification of DNA binding proteins(DNABPs)is considered a major challenge in genome annotation because they are linked to several important applied and research applications of cellular functions e.g.,in the study of the biological,biophysical,and biochemical effects of antibiotics,drugs,and steroids on *** paper presents an efficient approach for DNABPs identification based on deep transfer learning,named“DTLM-DBP.”Two transfer learning methods are used in the identification *** first is based on the pre-trained deep learning model as a feature’s extractor and *** different pre-trained Convolutional Neural Networks(CNN),AlexNet 8 and VGG 16,are tested and *** second method uses the deep learning model as a feature’s extractor only and two different classifiers for the identification *** classifiers,Support Vector Machine(SVM)and Random Forest(RF),are tested and *** proposed approach is tested using different DNA proteins *** performance of the identification process is evaluated in terms of identification accuracy,sensitivity,specificity and MCC,with four available DNA proteins datasets:PDB1075,PDB186,PDNA-543,and *** results show that the RF classifier,with VGG-Net pre-trained deep transfer learning features,gives the highest ***-DBP was compared with other published methods and it provides a considerable improvement in the performance of DNABPs identification.

关键词： DNABPs deep transfer learning AlexNet 8 VGG 16 SVM RF

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Robust deep transfer learning Based Object Detection and Tracking Approach

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Intelligent Automation & Soft Computing 2023年第3期35卷 3613-3626页

作者： C.Narmadha T.Kavitha R.Poonguzhali V.Hamsadhwani Ranjan walia Monia B.Jegajothi Electronics and Communication Engineering Periyar Maniammai Institute of Science&Technology(PMIST)Thanjavur613403India Department of Computer Science and Engineering Periyar Maniammai Institute of Science&TechnologyThanjavur613403India Department of Electrical and Electronics Engineering Periyar Maniammai Institute of Science and TechnologyThanjavur613403India Electrical Engineering Department Model Institute of Engineering&Technology(Autonomous)Jammu181122India Department of Computer Science and Engineering Shri Mata Vaishno Devi UniversityKatra182320India Center for Energy Research Chennai Institute of TechnologyChennai600069India

At present days,object detection and tracking concepts have gained more importance among researchers and business ***,deep learning(DL)approaches have been used for object tracking as it increases the perfor-mance and speed of the tracking *** paper presents a novel robust DL based object detection and tracking algorithm using Automated Image Anno-tation with ResNet based Faster regional convolutional neural network(R-CNN)named(AIA-FRCNN)*** AIA-RFRCNN method performs image anno-tation using a Discriminative Correlation Filter(DCF)with Channel and Spatial Reliability tracker(CSR)called DCF-CSRT *** AIA-RFRCNN model makes use of Faster RCNN as an object detector and tracker,which involves region proposal network(RPN)and Fast *** RPN is a full convolution network that concurrently predicts the bounding box and score of different *** RPN is a trained model used for the generation of the high-quality region proposals,which are utilized by Fast R-CNN for detection ***,Residual Network(ResNet 101)model is used as a shared convolutional neural network(CNN)for the generation of feature *** performance of the ResNet 101 model is further improved by the use of Adam optimizer,which tunes the hyperparameters namely learning rate,batch size,momentum,and weight ***,softmax layer is applied to classify the *** performance of the AIA-RFRCNN method has been assessed using a benchmark dataset and a detailed comparative analysis of the results takes *** outcome of the experiments indicated the superior characteristics of the AIA-RFRCNN model under diverse aspects.

关键词： Object detection tracking deep learning deep transfer learning image annotation

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Combining machine and deep transfer learning for mediastinal lymph node evaluation in patients with lung cancer

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虚拟现实与智能硬件（中英文） 2024年第3期6卷 226-238页

作者： Hui XIE Jianfang ZHANG Lijuan DING Tao TAN Qing LI Department of Radiation Oncology Affiliated Hospital(Clinical College)of Xiangnan UniversityChenzhou 423000China Faulty of Applied Sciences Macao Polytechnic UniversityMacao 999078China Department of Physical examination Beihu Centers for Disease Control and PreventionChenzhou 423000China Department of Radiation Oncology the Third People's Hospital of Chenzhou CityChenzhou 423000China School of Medical Imaging Laboratory Science and RehabilitationXiangnan UniversityChenzhou 423000China

Background The prognosis and survival of patients with lung cancer are likely to deteriorate with *** deep-learning in the detection of lymph node metastasis can facilitate the noninvasive calculation of the likelihood of such metastasis,thereby providing clinicians with crucial information to enhance diagnostic precision and ultimately improve patient survival and *** In total,623 eligible patients were recruited from two medical *** deep learning models,namely Alex,GoogLeNet,Resnet18,Resnet101,Vgg16,Vgg19,and MobileNetv3(small),were utilized to extract deep image histological *** dimensionality of the extracted features was then reduced using the Spearman correlation coefficient(r≥0.9)and Least Absolute Shrinkage and Selection *** machine learning methods,namely Support Vector Machine,K-nearest neighbor,Random Forest,Extra Trees,XGBoost,LightGBM,Naive Bayes,AdaBoost,Gradient Boosting Decision Tree,Linear Regression,and Multilayer Perceptron,were employed to construct classification prediction models for the filtered final *** diagnostic performances of the models were assessed using various metrics,including accuracy,area under the receiver operating characteristic curve,sensitivity,specificity,positive predictive value,and negative predictive *** and decision-curve analyses were also *** The present study demonstrated that using deep radiomic features extracted from Vgg16,in conjunction with a prediction model constructed via a linear regression algorithm,effectively distinguished the status of mediastinal lymph nodes in patients with lung *** performance of the model was evaluated based on various metrics,including accuracy,area under the receiver operating characteristic curve,sensitivity,specificity,positive predictive value,and negative predictive value,which yielded values of 0.808,0.834,0.851,0.745,0.829,and 0.776,*** validation set of the

关键词： Machine learning deep transfer learning Evaluation Mediastinal lymph node lung cancer patie

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deep transfer learning-based Fault Diagnosis of Spacecraft Attitude System

Deep Transfer Learning-based Fault Diagnosis of Spacecraft A...

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第三十九届中国控制会议

作者： Yifan Tang Liqian Dou Ruilong Zhang Xiuyun Zhang Wenjing Liu School of Electrical Engineering and Automation Science and Technology on Space Intelligent Control Laboratory Beijing Institute of Control Engineering

In recent years, an increasingly popularity of deep learning model for intelligent state monitoring, diagnosis and prediction of spacecraft has been observed. However, in the previous studies, a major assumption accepted by default is that source domain data and target domain data have same feature distribution. Unfortunately, this assumption is mostly invalid in real application. Considering the problem that the original fault data sample is small, the noise is high and the fault signal is unlabeled, in this paper, we propose deep transfer learning-based fault diagnosis method for spacecraft system in which a new fault diagnosis framework-deep transfer network（DTN） is built, and it can generalize deep learning model to domain adaptation scenario inspired by the idea of transfer learning. In order to improve the accuracy of on-orbit spacecraft fault data detection,the proposed framework with joint distribution adaptation（JDA） is applied to exploit the distribution structure of unlabeled data in target domain by using the data with labels in source domain. By comparing with other methods, the deep transfer network based on joint distribution adaptation has better transfer performance in fault diagnosis of spacecraft.

关键词： deep transfer learning joint distribution adaption convolutional neural network

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