An Electronic Component Recognition Algorithm Based on Deep Learning with a Faster SqueezeNet

Electronic component recognition plays an important role in industrial production, electronic manufacturing, and testing. In order to address the problem of the low recognition recall and accuracy of traditional image recognition technologies (such as principal component analysis (PCA) and support vector machine (SVM)), this paper selects multiple deep learning networks for testing and optimizes the SqueezeNet network. The paper then presents an electronic component recognition algorithm based on the Faster SqueezeNet network. This structure can reduce the size of network parameters and computational complexity without deteriorating the performance of the network. The results show that the proposed algorithm performs well, where the Receiver Operating Characteristic Curve (ROC) and Area Under the Curve (AUC), capacitor and inductor, reach 1.0. When the FPR is less than or equal 10 − 6 level, the TPR is greater than or equal to 0.99; its reasoning time is about 2.67 ms, achieving the industrial application level in terms of time consumption and performance.

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Raman Spectral Signatures of Serum-Derived Extracellular Vesicle-Enriched Isolates May Support the Diagnosis of CNS Tumors

Cancers ◽

10.3390/cancers13061407 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1407

Author(s):

Matyas Bukva ◽

Gabriella Dobra ◽

Juan Gomez-Perez ◽

Krisztian Koos ◽

Maria Harmati ◽

...

Keyword(s):

Lumbar Disc ◽

Area Under The Curve ◽

Principal Component ◽

Extracellular Vesicle ◽

Classification Performance ◽

Cns Tumors ◽

Clinical Samples ◽

Support Vector ◽

Serum Samples ◽

Raman Spectroscopic

Investigating the molecular composition of small extracellular vesicles (sEVs) for tumor diagnostic purposes is becoming increasingly popular, especially for diseases for which diagnosis is challenging, such as central nervous system (CNS) malignancies. Thorough examination of the molecular content of sEVs by Raman spectroscopy is a promising but hitherto barely explored approach for these tumor types. We attempt to reveal the potential role of serum-derived sEVs in diagnosing CNS tumors through Raman spectroscopic analyses using a relevant number of clinical samples. A total of 138 serum samples were obtained from four patient groups (glioblastoma multiforme, non-small-cell lung cancer brain metastasis, meningioma and lumbar disc herniation as control). After isolation, characterization and Raman spectroscopic assessment of sEVs, the Principal Component Analysis–Support Vector Machine (PCA–SVM) algorithm was performed on the Raman spectra for pairwise classifications. Classification accuracy (CA), sensitivity, specificity and the Area Under the Curve (AUC) value derived from Receiver Operating Characteristic (ROC) analyses were used to evaluate the performance of classification. The groups compared were distinguishable with 82.9–92.5% CA, 80–95% sensitivity and 80–90% specificity. AUC scores in the range of 0.82–0.9 suggest excellent and outstanding classification performance. Our results support that Raman spectroscopic analysis of sEV-enriched isolates from serum is a promising method that could be further developed in order to be applicable in the diagnosis of CNS tumors.

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Deep Learning Associated with Laser-Induced Breakdown Spectroscopy (LIBS) for the Prediction of Lead in Soil

Applied Spectroscopy ◽

10.1177/0003702819826283 ◽

2019 ◽

Vol 73 (5) ◽

pp. 565-573 ◽

Cited By ~ 9

Author(s):

Yun Zhao ◽

Mahamed Lamine Guindo ◽

Xing Xu ◽

Miao Sun ◽

Jiyu Peng ◽

...

Keyword(s):

Deep Learning ◽

Confusion Matrix ◽

Principal Component ◽

Classification Performance ◽

Soil Samples ◽

Laser Induced Breakdown Spectroscopy ◽

Support Vector ◽

Breakdown Spectroscopy ◽

Laser Induced Breakdown ◽

Different Levels

In this study, a method based on laser-induced breakdown spectroscopy (LIBS) was developed to detect soil contaminated with Pb. Different levels of Pb were added to soil samples in which tobacco was planted over a period of two to four weeks. Principal component analysis and deep learning with a deep belief network (DBN) were implemented to classify the LIBS data. The robustness of the method was verified through a comparison with the results of a support vector machine and partial least squares discriminant analysis. A confusion matrix of the different algorithms shows that the DBN achieved satisfactory classification performance on all samples of contaminated soil. In terms of classification, the proposed method performed better on samples contaminated for four weeks than on those contaminated for two weeks. The results show that LIBS can be used with deep learning for the detection of heavy metals in soil.

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A Novel Design of Classification of Coronary Artery Disease Using Deep Learning and Data Mining Algorithms

Revue d intelligence artificielle ◽

10.18280/ria.350304 ◽

2021 ◽

Vol 35 (3) ◽

pp. 209-215

Author(s):

Pratibha Verma ◽

Vineet Kumar Awasthi ◽

Sanat Kumar Sahu

Keyword(s):

Neural Network ◽

Data Mining ◽

Deep Learning ◽

Regression Tree ◽

Principal Component ◽

Classification And Regression Tree ◽

Support Vector ◽

Data Mining Algorithms ◽

R Programming ◽

Hidden Layer

Data mining techniques are included with Ensemble learning and deep learning for the classification. The methods used for classification are, Single C5.0 Tree (C5.0), Classification and Regression Tree (CART), kernel-based Support Vector Machine (SVM) with linear kernel, ensemble (CART, SVM, C5.0), Neural Network-based Fit single-hidden-layer neural network (NN), Neural Networks with Principal Component Analysis (PCA-NN), deep learning-based H2OBinomialModel-Deeplearning (HBM-DNN) and Enhanced H2OBinomialModel-Deeplearning (EHBM-DNN). In this study, experiments were conducted on pre-processed datasets using R programming and 10-fold cross-validation technique. The findings show that the ensemble model (CART, SVM and C5.0) and EHBM-DNN are more accurate for classification, compared with other methods.

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Deep Learning Radiomics to Predict Regional Lymph Node Staging for Hilar Cholangiocarcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.721460 ◽

2021 ◽

Vol 11 ◽

Author(s):

Yubizhuo Wang ◽

Jiayuan Shao ◽

Pan Wang ◽

Lintao Chen ◽

Mingliang Ying ◽

...

Keyword(s):

Deep Learning ◽

Lymph Node ◽

Hilar Cholangiocarcinoma ◽

High Performance ◽

Regional Lymph Node ◽

Characteristic Curve ◽

Support Vector ◽

Lymph Node Staging ◽

Computed Tomography Images ◽

External Test

BackgroundOur aim was to establish a deep learning radiomics method to preoperatively evaluate regional lymph node (LN) staging for hilar cholangiocarcinoma (HC) patients. Methods and MaterialsOf the 179 enrolled HC patients, 90 were pathologically diagnosed with lymph node metastasis. Quantitative radiomic features and deep learning features were extracted. An LN metastasis status classifier was developed through integrating support vector machine, high-performance deep learning radiomics signature, and three clinical characteristics. An LN metastasis stratification classifier (N1 vs. N2) was also proposed with subgroup analysis.ResultsThe average areas under the receiver operating characteristic curve (AUCs) of the LN metastasis status classifier reached 0.866 in the training cohort and 0.870 in the external test cohorts. Meanwhile, the LN metastasis stratification classifier performed well in predicting the risk of LN metastasis, with an average AUC of 0.946.ConclusionsTwo classifiers derived from computed tomography images performed well in predicting LN staging in HC and will be reliable evaluation tools to improve decision-making.

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Key Feature Recognition Algorithm of Network Intrusion Signal Based on Neural Network and Support Vector Machine

Symmetry ◽

10.3390/sym11030380 ◽

2019 ◽

Vol 11 (3) ◽

pp. 380 ◽

Cited By ~ 6

Author(s):

Kai Ye

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Feature Recognition ◽

False Positive Rate ◽

Principal Component ◽

Recognition Algorithm ◽

Support Vector ◽

Data Set ◽

Network Intrusion ◽

Key Features

When identifying the key features of the network intrusion signal based on the GA-RBF algorithm (using the genetic algorithm to optimize the radial basis) to identify the key features of the network intrusion signal, the pre-processing process of the network intrusion signal data is neglected, resulting in an increase in network signal data noise, reducing the accuracy of key feature recognition. Therefore, a key feature recognition algorithm for network intrusion signals based on neural network and support vector machine is proposed. The principal component neural network (PCNN) is used to extract the characteristics of the network intrusion signal and the support vector machine multi-classifier is constructed. The feature extraction result is input into the support vector machine classifier. Combined with PCNN and SVM (Support Vector Machine) algorithms, the key features of network intrusion signals are identified. The experimental results show that the algorithm has the advantages of high precision, low false positive rate and the recognition time of key features of R2L (it is a common way of network intrusion attack) data set is only 3.18 ms.

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Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury

Frontiers in Bioengineering and Biotechnology ◽

10.3389/fbioe.2020.562677 ◽

2020 ◽

Vol 8 ◽

Author(s):

Ting Li ◽

Weida Tong ◽

Ruth Roberts ◽

Zhichao Liu ◽

Shraddha Thakkar

Keyword(s):

Deep Learning ◽

Liver Injury ◽

High Throughput ◽

Permutation Test ◽

Characteristic Curve ◽

Machine Learning Algorithms ◽

Attrition Rate ◽

Support Vector ◽

Drug Induced ◽

Drug Induced Liver Injury

Drug-induced liver injury (DILI) is one of the most cited reasons for the high drug attrition rate and drug withdrawal from the market. The accumulated large amount of high throughput transcriptomic profiles and advances in deep learning provide an unprecedented opportunity to improve the suboptimal performance of DILI prediction. In this study, we developed an eight-layer Deep Neural Network (DNN) model for DILI prediction using transcriptomic profiles of human cell lines (LINCS L1000 dataset) with the current largest binary DILI annotation data [i.e., DILI severity and toxicity (DILIst)]. The developed models were evaluated by Monte Carlo cross-validation (MCCV), permutation test, and an independent validation (IV) set. The developed DNN model achieved the area under the receiver operating characteristic curve (AUC) of 0.802 and 0.798, and balanced accuracy of 0.741 and 0.721 for training and an IV set, respectively, outperforming the conventional machine learning algorithms, including K-nearest neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF). Moreover, the developed DNN model provided a more balanced sensitivity of 0.839 and specificity of 0.603. Besides, we found the developed DNN model had a superior predictive performance for oncology drugs. Also, the functional and network analysis of genes driving the predictions revealed their relevance to the underlying mechanisms of DILI. The proposed DNN model could be a promising tool for early detection of DILI potential in the pre-clinical setting.

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Fusion High-Resolution Network for Diagnosing ChestX-ray Images

Electronics ◽

10.3390/electronics9010190 ◽

2020 ◽

Vol 9 (1) ◽

pp. 190 ◽

Cited By ~ 3

Author(s):

Zhiwei Huang ◽

Jinzhao Lin ◽

Liming Xu ◽

Huiqian Wang ◽

Tong Bai ◽

...

Keyword(s):

Neural Networks ◽

Deep Learning ◽

High Resolution ◽

Convolutional Neural Networks ◽

Characteristic Curve ◽

Area Under The Curve ◽

Disease Classification ◽

Deep Convolutional Neural Networks ◽

Learning Methods ◽

Global And Local

The application of deep convolutional neural networks (CNN) in the field of medical image processing has attracted extensive attention and demonstrated remarkable progress. An increasing number of deep learning methods have been devoted to classifying ChestX-ray (CXR) images, and most of the existing deep learning methods are based on classic pretrained models, trained by global ChestX-ray images. In this paper, we are interested in diagnosing ChestX-ray images using our proposed Fusion High-Resolution Network (FHRNet). The FHRNet concatenates the global average pooling layers of the global and local feature extractors—it consists of three branch convolutional neural networks and is fine-tuned for thorax disease classification. Compared with the results of other available methods, our experimental results showed that the proposed model yields a better disease classification performance for the ChestX-ray 14 dataset, according to the receiver operating characteristic curve and area-under-the-curve score. An ablation study further confirmed the effectiveness of the global and local branch networks in improving the classification accuracy of thorax diseases.

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Structured report data can be used to develop deep learning algorithms: a proof of concept in ankle radiographs

Insights into Imaging ◽

10.1186/s13244-019-0777-8 ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 5

Author(s):

Daniel Pinto dos Santos ◽

Sebastian Brodehl ◽

Bettina Baeßler ◽

Gordon Arnhold ◽

Thomas Dratsch ◽

...

Keyword(s):

Deep Learning ◽

Language Processing ◽

Learning Algorithms ◽

Area Under The Curve ◽

Learning Networks ◽

Report Data ◽

Structured Reports ◽

Picture Archiving And Communication ◽

Using Data

Abstract Background Data used for training of deep learning networks usually needs large amounts of accurate labels. These labels are usually extracted from reports using natural language processing or by time-consuming manual review. The aim of this study was therefore to develop and evaluate a workflow for using data from structured reports as labels to be used in a deep learning application. Materials and methods We included all plain anteriorposterior radiographs of the ankle for which structured reports were available. A workflow was designed and implemented where a script was used to automatically retrieve, convert, and anonymize the respective radiographs of cases where fractures were either present or absent from the institution’s picture archiving and communication system (PACS). These images were then used to retrain a pretrained deep convolutional neural network. Finally, performance was evaluated on a set of previously unseen radiographs. Results Once implemented and configured, completion of the whole workflow took under 1 h. A total of 157 structured reports were retrieved from the reporting platform. For all structured reports, corresponding radiographs were successfully retrieved from the PACS and fed into the training process. On an unseen validation subset, the model showed a satisfactory performance with an area under the curve of 0.850 (95% CI 0.634–1.000) for detection of fractures. Conclusion We demonstrate that data obtained from structured reports written in clinical routine can be used to successfully train deep learning algorithms. This highlights the potential role of structured reporting for the future of radiology, especially in the context of deep learning.

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Development, Validation, and Reliability of a Sedation Scale in Horses (EquiSed)

Frontiers in Veterinary Science ◽

10.3389/fvets.2021.611729 ◽

2021 ◽

Vol 8 ◽

Author(s):

Alice Rodrigues de Oliveira ◽

Miguel Gozalo-Marcilla ◽

Simone Katja Ringer ◽

Stijn Schauvliege ◽

Mariana Werneck Fonseca ◽

...

Keyword(s):

Controlled Trial ◽

Interobserver Reliability ◽

Characteristic Curve ◽

Intraclass Correlation ◽

Area Under The Curve ◽

Principal Component ◽

Load Factor ◽

Spearman Correlation ◽

Numerical Rating ◽

Head Height

The lack of standardization of sedation scales in horses limits the reproducibility between different studies. This prospective, randomized, blinded, horizontal and controlled trial aimed to validate a scale for sedation in horses (EquiSed). Seven horses were treated with intravenous detomidine in low/high doses alone (DL 2.5 μg/kg + 6.25 μg/kg/h; DH 5 μg/kg +12.5 μg/kg/h) or associated with methadone (DLM and DHM, 0.2 mg/kg + 0.05 mg/kg/h) and with low (ACPL 0.02 mg/kg) or high (ACPH 0.09 mg/kg) doses of acepromazine alone. Horses were filmed at (i) baseline (ii) peak, (iii) intermediate, and (iv) end of sedation immediately before auditory, visual and pressure stimuli were applied and postural instability evaluated for another study. Videos were randomized and blindly evaluated by four evaluators in two phases with 1-month interval. Intra- and interobserver reliability of the sum of EquiSed (Intraclass correlation coefficient) ranged between 0.84–0.94 and 0.45–0.88, respectively. The criterion validity was endorsed by the high Spearman correlation between the EquiSed and visual analog (0.77), numerical rating (0.76), and simple descriptive scales (0.70), and average correlation with head height above the ground (HHAG) (−0.52). The Friedman test confirmed the EquiSed responsiveness over time. The principal component analysis showed that all items of the scale had a load factor ≥ 0.50. The item-total Spearman correlation for all items ranged from 0.3 to 0.5, and the internal consistency was good (Cronbach's α = 0.73). The area under the curve of EquiSed HHAG as a predictive diagnostic measure was 0.88. The sensitivity of the EquiSed calculated according to the cut-off point (score 7 of the sum of the EquiSed) determined by the receiver operating characteristic curve, was 96% and specificity was 83%. EquiSed has good intra- and interobserver reliabilities and is valid to evaluate tranquilization and sedation in horses.

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Deep Learning versus Conventional Machine Learning for Detection of Healthcare-Associated Infections in French Clinical Narratives

Methods of Information in Medicine ◽

10.1055/s-0039-1677692 ◽

2019 ◽

Vol 58 (01) ◽

pp. 031-041 ◽

Cited By ~ 4

Author(s):

Sara Rabhi ◽

Jérémie Jakubowicz ◽

Marie-Helene Metzger

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Characteristic Curve ◽

Bayesian Optimization ◽

Gradient Boosting ◽

Support Vector ◽

University Hospitals ◽

Infection Control Practitioner ◽

Medical Reports ◽

Conventional Machine

Objective The objective of this article was to compare the performances of health care-associated infection (HAI) detection between deep learning and conventional machine learning (ML) methods in French medical reports. Methods The corpus consisted in different types of medical reports (discharge summaries, surgery reports, consultation reports, etc.). A total of 1,531 medical text documents were extracted and deidentified in three French university hospitals. Each of them was labeled as presence (1) or absence (0) of HAI. We started by normalizing the records using a list of preprocessing techniques. We calculated an overall performance metric, the F1 Score, to compare a deep learning method (convolutional neural network [CNN]) with the most popular conventional ML models (Bernoulli and multi-naïve Bayes, k-nearest neighbors, logistic regression, random forests, extra-trees, gradient boosting, support vector machines). We applied the hyperparameter Bayesian optimization for each model based on its HAI identification performances. We included the set of text representation as an additional hyperparameter for each model, using four different text representations (bag of words, term frequency–inverse document frequency, word2vec, and Glove). Results CNN outperforms all other conventional ML algorithms for HAI classification. The best F1 Score of 97.7% ± 3.6% and best area under the curve score of 99.8% ± 0.41% were achieved when CNN was directly applied to the processed clinical notes without a pretrained word2vec embedding. Through receiver operating characteristic curve analysis, we could achieve a good balance between false notifications (with a specificity equal to 0.937) and system detection capability (with a sensitivity equal to 0.962) using the Youden's index reference. Conclusions The main drawback of CNNs is their opacity. To address this issue, we investigated CNN inner layers' activation values to visualize the most meaningful phrases in a document. This method could be used to build a phrase-based medical assistant algorithm to help the infection control practitioner to select relevant medical records. Our study demonstrated that deep learning approach outperforms other classification learning algorithms for automatically identifying HAIs in medical reports.

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