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

Background The prognosis and survival of patients with lung cancer are likely to deteriorate with metastasis.Using 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 prognosis.Methods In total,623 eligible patients were recruited from two medical institutions.Seven deep learning models,namely Alex,GoogLeNet,Resnet18,Resnet101,Vgg16,Vgg19,and MobileNetv3(small),were utilized to extract deep image histological features.The dimensionality of the extracted features was then reduced using the Spearman correlation coefficient(r≥0.9)and Least Absolute Shrinkage and Selection Operator.Eleven 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 features.The 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 value.Calibration and decision-curve analyses were also performed.Results 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 cancer.The 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,respectively.The validation set of the model was assessed using clinical decision curves,calibration curves,and confusion matrices,which collectively demonstrated the model's stability and accuracy.Conclusion In this study,information on the deep radiomics of Vgg16 was obtained from computed tomography images,and the linear regression method was able to accurately diagnose mediastinal lymph node metastases in patients with lung cancer.

Enhancing Pneumonia Detection in Pediatric Chest X-Rays Using CGAN-Augmented Datasets and Lightweight Deep Transfer Learning Models

Pneumonia ranks as a leading cause of mortality, particularly in children aged five and under. Detecting this disease typically requires radiologists to examine chest X-rays and report their findings to physicians, a task susceptible to human error. The application of Deep Transfer Learning (DTL) for the identification of pneumonia through chest X-rays is hindered by a shortage of available images, which has led to less than optimal DTL performance and issues with overfitting. Overfitting is characterized by a model’s learning that is too closely fitted to the training data, reducing its effectiveness on unseen data. The problem of overfitting is especially prevalent in medical image processing due to the high costs and extensive time required for image annotation, as well as the challenge of collecting substantial datasets that also respect patient privacy concerning infectious diseases such as pneumonia. To mitigate these challenges, this paper introduces the use of conditional generative adversarial networks (CGAN) to enrich the pneumonia dataset with 2690 synthesized X-ray images of the minority class, aiming to even out the dataset distribution for improved diagnostic performance. Subsequently, we applied four modified lightweight deep transfer learning models such as Xception, MobileNetV2, MobileNet, and EfficientNetB0. These models have been fine-tuned and evaluated, demonstrating remarkable detection accuracies of 99.26%, 98.23%, 97.06%, and 94.55%, respectively, across fifty epochs. The experimental results validate that the models we have proposed achieve high detection accuracy rates, with the best model reaching up to 99.26% effectiveness, outperforming other models in the diagnosis of pneumonia from X-ray images.

Optimal Deep Transfer Learning Based Colorectal Cancer Detection and Classification Model

Colorectal carcinoma(CRC)is one such dispersed cancer globally and also prominent one in causing cancer-based death.Conventionally,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 images.This study develops a slime mould algorithm with deep transfer learning enabled colorectal cancer detection and classification(SMADTL-CCDC)algorithm.The presented SMADTL-CCDC technique intends to appropriately recognize the occurrence of colorectal cancer.To accomplish this,the SMADTLCCDC model initially undergoes pre-processing to improve the input image quality.In addition,a dense-EfficientNet technique was employed to extract feature vectors from the pre-processed images.Moreover,SMA with Discrete Hopfield neural network(DHNN)method was applied for the recognition and classification of colorectal cancer.The 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.

Robust Deep Transfer Learning Based Object Detection and Tracking Approach

At present days,object detection and tracking concepts have gained more importance among researchers and business people.Presently,deep learning(DL)approaches have been used for object tracking as it increases the perfor-mance and speed of the tracking process.This 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)model.The AIA-RFRCNN method performs image anno-tation using a Discriminative Correlation Filter(DCF)with Channel and Spatial Reliability tracker(CSR)called DCF-CSRT model.The AIA-RFRCNN model makes use of Faster RCNN as an object detector and tracker,which involves region proposal network(RPN)and Fast R-CNN.The RPN is a full convolution network that concurrently predicts the bounding box and score of different objects.The RPN is a trained model used for the generation of the high-quality region proposals,which are utilized by Fast R-CNN for detection process.Besides,Residual Network(ResNet 101)model is used as a shared convolutional neural network(CNN)for the generation of feature maps.The 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 decay.Finally,softmax layer is applied to classify the images.The performance of the AIA-RFRCNN method has been assessed using a benchmark dataset and a detailed comparative analysis of the results takes place.The outcome of the experiments indicated the superior characteristics of the AIA-RFRCNN model under diverse aspects.

Cross-Band Spectrum Prediction Based on Deep Transfer Learning

Spectrum prediction is a promising technology to infer future spectrum state by exploiting inherent patterns of historical spectrum data.In practice,for a given spectrum band of interest,when facing relatively scarce historical data,spectrum prediction based on traditional learning methods does not work well.Thus,this paper proposes a cross-band spectrum prediction model based on transfer learning.Firstly,by analysing service activities and computing the distances between various frequency points based on Dynamic Time Warping,the similarity between spectrum bands has been verified.Next,the features,which mainly affect the performance of transfer learning in the crossband spectrum prediction,are explored by leveraging transfer component analysis.Then,the effectiveness of transfer learning for the cross-band spectrum prediction has been demonstrated.Further,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.

Spectrum Prediction Based on GAN and Deep Transfer Learning:A Cross-Band Data Augmentation Framework

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 changes.The previously trained model for prediction often cannot maintain a good performance when facing small amount of historical data of the new target frequency.Moreover,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 limited.To 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 learning.Firstly,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 band.Then,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 GAN.Finally,experiment results demonstrate the effectiveness of the proposed framework.

Classification of Citrus Plant Diseases Using Deep Transfer Learning

In recent years,the field of deep learning has played an important role towards automatic detection and classification of diseases in vegetables and fruits.This in turn has helped in improving the quality and production of vegetables and fruits.Citrus fruits arewell known for their taste and nutritional values.They are one of the natural and well known sources of vitamin C and planted worldwide.There are several diseases which severely affect the quality and yield of citrus fruits.In this paper,a new deep learning based technique is proposed for citrus disease classification.Two different pre-trained deep learning models have been used in this work.To increase the size of the citrus dataset used in this paper,image augmentation techniques are used.Moreover,to improve the visual quality of images,hybrid contrast stretching has been adopted.In addition,transfer learning is used to retrain the pre-trainedmodels and the feature set is enriched by using feature fusion.The 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 plants.The proposed technique attains a classification accuracy of 95.7%with superior results when compared with recent techniques.

DTLM-DBP:Deep Transfer Learning Models for DNA Binding Proteins Identification

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 DNA.This 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 process.The first is based on the pre-trained deep learning model as a feature’s extractor and classifier.Two different pre-trained Convolutional Neural Networks(CNN),AlexNet 8 and VGG 16,are tested and compared.The second method uses the deep learning model as a feature’s extractor only and two different classifiers for the identification process.Two classifiers,Support Vector Machine(SVM)and Random Forest(RF),are tested and compared.The proposed approach is tested using different DNA proteins datasets.The 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 PDNA-316.The results show that the RF classifier,with VGG-Net pre-trained deep transfer learning features,gives the highest performance.DTLM-DBP was compared with other published methods and it provides a considerable improvement in the performance of DNABPs identification.

Deep Transfers of p-Class Tower Groups

Let p be a prime. For any finite p-group G, the deep transfers T H,G ' : H / H ' → G ' / G " from the maximal subgroups H of index (G:H) = p in G to the derived subgroup G ' are introduced as an innovative tool for identifying G uniquely by means of the family of kernels ùd(G) =(ker(T H,G ')) (G: H) = p. For all finite 3-groups G of coclass cc(G) = 1, the family ùd(G) is determined explicitly. The results are applied to the Galois groups G =Gal(F3 (∞)/ F) of the Hilbert 3-class towers of all real quadratic fields F = Q(√d) with fundamental discriminants d > 1, 3-class group Cl3(F) □ C3 × C3, and total 3-principalization in each of their four unramified cyclic cubic extensions E/F. A systematic statistical evaluation is given for the complete range 1 d 7, and a few exceptional cases are pointed out for 1 d 8.

Multiconditional machining process quality prediction using deep transfer learning network

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.

Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation

The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source domain.However,in engineering scenarios,achieving such high-quality label annotation is difficult and expensive.The incorrect label annotation produces two negative effects:1)the complex decision boundary of diagnosis models lowers the generalization performance on the target domain,and2)the distribution of target domain samples becomes misaligned with the false-labeled samples.To overcome these negative effects,this article proposes a solution called the label recovery and trajectory designable network(LRTDN).LRTDN consists of three parts.First,a residual network with dual classifiers is to learn features from cross-domain samples.Second,an annotation check module is constructed to generate a label anomaly indicator that could modify the abnormal labels of false-labeled samples in the source domain.With the training of relabeled samples,the complexity of diagnosis model is reduced via semi-supervised learning.Third,the adaptation trajectories are designed for sample distributions across domains.This ensures that the target domain samples are only adapted with the pure-labeled samples.The LRTDN is verified by two case studies,in which the diagnosis knowledge of bearings is transferred across different working conditions as well as different yet related machines.The results show that LRTDN offers a high diagnosis accuracy even in the presence of incorrect annotation.

利用DeepLabv3+模型提取分析街景图像绿视率——以北京三环内为例

基于语义分割模型的绿视率提取缺乏适用性研究,本文首先基于DeepLabv3+语义分割预训练模型和自主标注样本,采用迁移学习策略,构建街景图像语义分割模型,并对其进行精度验证。然后基于构建的街景图像语义分割模型提取计算北京三环内绿视率(GVI),分析点、线尺度下绿视率空间分布特征。结果表明:①相比DeepLabv3+语义分割预训练模型,迁移学习后模型F1值和mIoU值分别提高了7%和3%;②点状尺度上北京三环内绿视率整体呈“西高东低,北高南低”聚类式分布特征,0~0.15区间内街景采样点GVI约占58.1%;③线状尺度上整体呈“环线低环内高”且中心发散式特征分布,0~0.15区间内研究区道路GVI约占59.8%。该研究对于提升城市街道绿化感知程度和城市空间规划具有重要的参考意义。

Ensemble deep transfer learning driven by multisensor signals for the fault diagnosis of bevel-gear cross-operation conditions

The existing intelligent fault diagnosis techniques of bevel gear focus on single-sensor signal analysis under the steady operation condition.In this study,a new method is proposed based on ensemble deep transfer learning and multisensor signals to enhance the fault diagnosis adaptability and reliability of bevel gear under various operation conditions.First,a novel stacked autoencoder(NSAE)is constructed using a denoising autoencoder,batch normalization,and the Swish activation function.Second,a series of source-domain NSAEs with multisensor vibration signals is pretrained.Third,the good model parameters provided by the source-domain NSAEs are transferred to initialize the corresponding target-domain NSAEs.Finally,a modified voting fusion strategy is designed to obtain a comprehensive result.The multisensor signals collected under the different operation conditions of bevel gear are used to verify the proposed method.The comparison results show that the proposed method can diagnose different faults in an accurate and stable manner using only one target-domain sample,thereby outperforming the existing methods.

Recognition of grape leaf diseases using MobileNetV3 and deep transfer learning

Timely diagnosis and accurate identification of grape leaf diseases are decisive for controlling the spread of disease and ensuring the healthy development of the grape industry.The objective of this research was to propose a simple and efficient approach to improve grape leaf disease identification accuracy with limited computing resources and scale of training image dataset based on deep transfer learning and an improved MobileNetV3 model(GLD-DTL).A pre-training model was obtained by training MobileNetV3 using the ImageNet dataset to extract common features of the grape leaves.And the last convolution layer of the pre-training model was modified by adding a batch normalization function.A dropout layer followed by a fully connected layer was used to improve the generalization ability of the pre-training model and realize a weight matrix to quantify the scores of six diseases,according to which the Softmax method was added as the top layer of the modified networks to give probability distribution of six diseases.Finally,the grape leaf diseases dataset,which was constructed by processing the image with data augmentation and image annotation technologies,was input into the modified networks to retrain the networks to obtain the grape leaf diseases recognition(GLDR)model.Results showed that the proposed GLD-DTL approach had better performance than some recent approaches.The identification accuracy was as high as 99.84%while the model size was as small as 30 MB.

Deep transfer network of heterogeneous domain feature in machine translation

In order to address the shortcoming of feature representation limitation in machine translation(MT)system,this paper presents a feature transfer method in MT.Meta feature transfer of the decoding process considered not only their own translation system,but also transferred knowledge of another translation system.The domain meta feature and the objective function of domain adaptation are used to better model the domain transfer task.In this paper,extensive experiments and comparisons are made.The experiment results show that the proposed model has a significant improvement in domain transfer task.The first model has better performance than baseline system,which improves 3.06 BLEU score on the news test set,improves 3.27 BLEU score on the education test set,and improves 3.93 BLEU score on the law test set;The second model improves 3.16 BLEU score on the news test set,improves 3.54 BLEU score on the education test set,and improves 4.2 BLEU score on the law test set.

A Hybrid Deep Fused Learning Approach to Segregate Infectious Diseases

Humankind is facing another deadliest pandemic of all times in history,caused by COVID-19.Apart from this challenging pandemic,World Health Organization(WHO)considers tuberculosis(TB)as a preeminent infectious disease due to its high infection rate.Generally,both TB and COVID-19 severely affect the lungs,thus hardening the job of medical practitioners who can often misidentify these diseases in the current situation.Therefore,the time of need calls for an immediate and meticulous automatic diagnostic tool that can accurately discriminate both diseases.As one of the preliminary smart health systems that examine three clinical states(COVID-19,TB,and normal cases),this study proposes an amalgam of image filtering,data-augmentation technique,transfer learning-based approach,and advanced deep-learning classifiers to effectively segregate these diseases.It first employed a generative adversarial network(GAN)and Crimmins speckle removal filter on X-ray images to overcome the issue of limited data and noise.Each pre-processed image is then converted into red,green,and blue(RGB)and Commission Internationale de l’Elcairage(CIE)color spaces from which deep fused features are formed by extracting relevant features using DenseNet121 and ResNet50.Each feature extractor extracts 1000 most useful features which are then fused and finally fed to two variants of recurrent neural network(RNN)classifiers for precise discrimination of threeclinical states.Comparative analysis showed that the proposed Bi-directional long-short-term-memory(Bi-LSTM)model dominated the long-short-termmemory(LSTM)network by attaining an overall accuracy of 98.22%for the three-class classification task,whereas LSTM hardly achieved 94.22%accuracy on the test dataset.

Estimating the State of Health for Lithium-ion Batteries:A Particle Swarm Optimization-Assisted Deep Domain Adaptation Approach

The state of health(SOH)is a critical factor in evaluating the performance of the lithium-ion batteries(LIBs).Due to various end-user behaviors,the LIBs exhibit different degradation modes,which makes it challenging to estimate the SOHs in a personalized way.In this article,we present a novel particle swarm optimization-assisted deep domain adaptation(PSO-DDA)method to estimate the SOH of LIBs in a personalized manner,where a new domain adaptation strategy is put forward to reduce cross-domain distribution discrepancy.The standard PSO algorithm is exploited to automatically adjust the chosen hyperparameters of developed DDA-based method.The proposed PSODDA method is validated by extensive experiments on two LIB datasets with different battery chemistry materials,ambient temperatures and charge-discharge configurations.Experimental results indicate that the proposed PSO-DDA method surpasses the convolutional neural network-based method and the standard DDA-based method.The Py Torch implementation of the proposed PSO-DDA method is available at https://github.com/mxt0607/PSO-DDA.

MDEV Model:A Novel Ensemble-Based Transfer Learning Approach for Pneumonia Classification Using CXR Images

Pneumonia is a dangerous respiratory disease due to which breathing becomes incredibly difficult and painful;thus,catching it early is crucial.Medical physicians’time is limited in outdoor situations due to many patients;therefore,automated systems can be a rescue.The input images from the X-ray equipment are also highly unpredictable due to variances in radiologists’experience.Therefore,radiologists require an automated system that can swiftly and accurately detect pneumonic lungs from chest x-rays.In medical classifications,deep convolution neural networks are commonly used.This research aims to use deep pretrained transfer learning models to accurately categorize CXR images into binary classes,i.e.,Normal and Pneumonia.The MDEV is a proposed novel ensemble approach that concatenates four heterogeneous transfer learning models:Mobile-Net,DenseNet-201,EfficientNet-B0,and VGG-16,which have been finetuned and trained on 5,856 CXR images.The evaluation matrices used in this research to contrast different deep transfer learning architectures include precision,accuracy,recall,AUC-roc,and f1-score.The model effectively decreases training loss while increasing accuracy.The findings conclude that the proposed MDEV model outperformed cutting-edge deep transfer learning models and obtains an overall precision of 92.26%,an accuracy of 92.15%,a recall of 90.90%,an auc-roc score of 90.9%,and f-score of 91.49%with minimal data pre-processing,data augmentation,finetuning and hyperparameter adjustment in classifying Normal and Pneumonia chests.

Cultivated land information extraction in UAV imagery based on deep convolutional neural network and transfer learning

The development of precision agriculture demands high accuracy and efficiency of cultivated land information extraction. As a new means of monitoring the ground in recent years, unmanned aerial vehicle(UAV) low-height remote sensing technique, which is flexible, efficient with low cost and with high resolution, is widely applied to investing various resources. Based on this, a novel extraction method for cultivated land information based on Deep Convolutional Neural Network and Transfer Learning(DTCLE) was proposed. First, linear features(roads and ridges etc.) were excluded based on Deep Convolutional Neural Network(DCNN). Next, feature extraction method learned from DCNN was used to cultivated land information extraction by introducing transfer learning mechanism. Last, cultivated land information extraction results were completed by the DTCLE and e Cognition for cultivated land information extraction(ECLE). The location of the Pengzhou County and Guanghan County, Sichuan Province were selected for the experimental purpose. The experimental results showed that the overall precision for the experimental image 1, 2 and 3(of extracting cultivated land) with the DTCLE method was 91.7%, 88.1% and 88.2% respectively, and the overall precision of ECLE is 90.7%, 90.5% and 87.0%, respectively. Accuracy of DTCLE was equivalent to that of ECLE, and also outperformed ECLE in terms of integrity and continuity.

Spectrum Characteristics and Transfer Function of the Hydrograph of the Deep Aqueous System

The fluctuation of most of the hydrograph in the deep aqueous system records the fluid pulsation in lithosphere and variation of the earth's crust. Many observations have verified that groundwater is an ideal information carrier of the crust. In this paper, the series of input (precipitation, air pressure, Earth tide etc.) and output (water level, artesian flow) of the deep aqueous system are studied by using the spectrum analysis and system theory. The application concepts of transfer function and the spectral structure of the hydrograph enrich the knowledge of the deep aqueous system. Two typical spectral structures of the hydrograph of the deep aqueous system are obtained by comparing with many water-bearing systems of the Jizhong depression. One is from well Ma-17 and the other is from the well Xinze-5. Finally, the physical models of forming the spectrum of the hydrograph are constructed on the basis of the spectrum research on the deep aqueous system.