An application of convolutional neural networks with salient features for relation classification

Dashdorj, Zolzaya; Song, Min

doi:10.1186/s12859-019-2808-3

Cited by 10 publications

(6 citation statements)

References 17 publications

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“…We compare the models in this study using a variety of error scores [26] for multi-class classification using a 1-vs rest approach [24] , [42] . Specifically, we are using: accuracy, precision, recall, F-score, and AUC-ROC, refer Table 3 .…”

Section: Methodsmentioning

confidence: 99%

Exploring COVID-related relationship extraction: Contrasting data sources and analyzing misinformation

Sharma,

Farea,

Perera

et al. 2024

Heliyon

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Section: Methodsmentioning

confidence: 99%

Exploring COVID-related relationship extraction: Contrasting data sources and analyzing misinformation

Sharma,

Farea,

Perera

et al. 2024

Heliyon

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“…Each sentence was converted into a text embedding T = [ t 1 ; t 2 ; · · ·; t L ] 2 RC × L , with each t i 2 V . The text embedding select token embedding, drop segment embedding, and position embedding ( 17 ) ( Figure 3 ).…”

Section: Methodsmentioning

confidence: 99%

Prediction and Diagnosis of Respiratory Disease by Combining Convolutional Neural Network and Bi-directional Long Short-Term Memory Methods

Alimu²,

Luan³

et al. 2022

Front. Public Health

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ObjectiveBased on the respiratory disease big data platform in southern Xinjiang, we established a model that predicted and diagnosed chronic obstructive pulmonary disease, bronchiectasis, pulmonary embolism and pulmonary tuberculosis, and provided assistance for primary physicians.MethodsThe method combined convolutional neural network (CNN) and long-short-term memory network (LSTM) for prediction and diagnosis of respiratory diseases. We collected the medical records of inpatients in the respiratory department, including: chief complaint, history of present illness, and chest computed tomography. Pre-processing of clinical records with “jieba” word segmentation module, and the Bidirectional Encoder Representation from Transformers (BERT) model was used to perform word vectorization on the text. The partial and total information of the fused feature set was encoded by convolutional layers, while LSTM layers decoded the encoded information.ResultsThe precisions of traditional machine-learning, deep-learning methods and our proposed method were 0.6, 0.81, 0.89, and F1 scores were 0.6, 0.81, 0.88, respectively.ConclusionCompared with traditional machine learning and deep-learning methods that our proposed method had a significantly higher performance, and provided precise identification of respiratory disease.

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“…The representative works for the traditional computeraided detection methods are Scale-Invariant Feature Transform (SIFT) [9] and Histogram of Oriented Gradients (HOG) [10]; The problem-solving process of these methods can be roughly summarized as the following three steps:…”

Section: A the Detection Of Lung Nodulesmentioning

confidence: 99%

STBi-YOLO: A Real-Time Object Detection Method for Lung Nodule Recognition

Liu

2022

IEEE Access

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Lung cancer is the most prevalent and deadly oncological disease in the world, but a timely detection of lung nodules can greatly improve the survival rate of this disease. However, due to the tiny size of lung nodules and inconspicuous edges, lung nodules are not easily distinguished by naked eyes thus medical image diagnosticians are prone to misdiagnosis simply based on their own experiences and subjective judgements. In recent years, the machine-learning-based image processing techniques find their wide applications in the field of medical diagnosis, and have been proved to be an efficient way to aid diagnosticians to accurately identify subtle lesions in images. To accurately recognize lung nodules in CT images, in this paper, we propose an approach, called STBi-YOLO. This approach stems from YOLO-v5, but makes significant improvements from three dimensions-we first use the spatial pyramid pooling network that is based on stochastic-pooling method to modify the basic network structure of YOLO-v5; then apply a bidirectional feature pyramid network to perform multi-scale feature fusion; finally improve the loss function of the YOLO-v5 and adopt the EIoU function to optimize the training model. To evaluate our approach, we compare STBi-YOLO with YOLO-v3, YOLO-v4, YOLO-v5, and multiple leading object detection models, such as Faster R-CNN and SSD. The experiments show that STBi-YOLO achieves an accuracy of 96.1% and a recall rate of 93.3% for the detection of lung nodules, while producing a 4× smaller model size in memory consumption than YOLO-v5 and exhibiting comparable results in terms of mAP and time cost against Faster R-CNN and SSD.

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An application of convolutional neural networks with salient features for relation classification

Cited by 10 publications

References 17 publications

Exploring COVID-related relationship extraction: Contrasting data sources and analyzing misinformation

Exploring COVID-related relationship extraction: Contrasting data sources and analyzing misinformation

Prediction and Diagnosis of Respiratory Disease by Combining Convolutional Neural Network and Bi-directional Long Short-Term Memory Methods

STBi-YOLO: A Real-Time Object Detection Method for Lung Nodule Recognition

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