Convolutional neural network: a review of models, methodologies and applications to object detection

Dhillon, Anamika; Verma, Gyanendra K.

doi:10.1007/s13748-019-00203-0

Cited by 725 publications

(368 citation statements)

References 101 publications

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“…In the field of computer vision research, deep learning techniques such as CNN [17] have been widely used in image classification. CNN can directly extract the information in the image layer by layer from a large amount of labeled data, extract the effective features of the image, and detect and classify the image.…”

Section: B Convolutional Neural Network For Human Activity Recognitionmentioning

confidence: 99%

A Computer Vision-Based Dual Network Approach for Indoor Fall Detection

Zhang¹,

Fang²,

Zhu³

2020

IJISRT

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In order to strengthen the monitoring of the elderly and reduce the safety risks caused by falls, a video-based indoor fall detection algorithm using a dual network structure is proposed. Firstly, for the recorded video stream, we apply the fine-tuned YOLACT network to extract the contours of the human body, and then design a simple convolutional neural network to distinguish the categories of different family activities (including bending, standing, sitting and lying), and finally make a fall decision. When a lying position is detected on the floor region, it is considered as a fall. Experiments show that the proposed algorithm can successfully detect fall events in different indoor scenarios, and have a low false detection rate on the constructed data set.

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Section: B Convolutional Neural Network For Human Activity Recognitionmentioning

confidence: 99%

A Computer Vision-Based Dual Network Approach for Indoor Fall Detection

Zhang¹,

Fang²,

Zhu³

2020

IJISRT

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“…The parameters of the deep convolutional neural network are defined as follows: Let L be the number of network layers. In the convolution layer, the size of the convolution kernel is K. The dimension of the convolution kernel matrix is defined as F. p represents the filling size, and the steps of convolution kernel moving are S [28]. Before the data is transferred to the convolution layer, it is necessary to fill in missing data.…”

Section: The Derivation Of Deep Ccnmentioning

confidence: 99%

“…Because of its powerful feature extraction ability, it can mine deeper features from a large number of training data with the hierarchical network structure, so as to extract the feature information that cannot be obtained by traditional classifiers. Therefore, it has been widely used in speech recognition, image recognition, text detection, and so on [18,[25][26][27][28][29][30][31][32][33]. As we know, the medical data set has the characteristics of large amount of data and rich features, so it is helpful to discover potential medical laws and valuable information among medical data by applying deep convolutional neural network to medical data.…”

Section: Introductionmentioning

confidence: 99%

Research of Epidemic Big Data Based on Improved Deep Convolutional Neural Network

Wang

2020

Computational and Mathematical Methods in Medicine

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In recent years, with the acceleration of the aging process and the aggravation of life pressure, the proportion of chronic epidemics has gradually increased. A large amount of medical data will be generated during the hospitalization of diabetics. It will have important practical significance and social value to discover potential medical laws and valuable information among medical data. In view of this, an improved deep convolutional neural network (“CNN+” for short) algorithm was proposed to predict the changes of diabetes. Firstly, the bagging integrated classification algorithm was used instead of the output layer function of the deep CNN, which can help the improved deep CNN algorithm constructed for the data set of diabetic patients and improve the accuracy of classification. In this way, the “CNN+” algorithm can take the advantages of both the deep CNN and the bagging algorithm. On the one hand, it can extract the potential features of the data set by using the powerful feature extraction ability of deep CNN. On the other hand, the bagging integrated classification algorithm can be used for feature classification, so as to improve the classification accuracy and obtain better disease prediction effect to assist doctors in diagnosis and treatment. Experimental results show that compared with the traditional convolutional neural network and other classification algorithm, the “CNN+” model can get more reliable prediction results.

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“…Besides the huge success of deep learning models in the computer vision field for common everyday objects, many researchers introduced deep learning into the medical imaging field [13,14,15], especially for the images of the diagnostic radiology field. Traditional deep learning neural networks including RNN [16], CNN [17,18], resNet [19] and quite a few noval schemas, like FCN, Unet, SegNet, Unet-3D and Mask-RCNN [20] were widely used in segmentation studies in X-Ra [21], PET/CT [22,23], MRI [24], and Ultrasound [25].…”

Section: Introductionmentioning

confidence: 99%

Deep learning segmentation model for automated detection of the opacity regions in the chest X-rays of the Covid-19 positive patients and the application for disease severity

Tang

Sun

2020

Preprint

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The pandemic of Covid-19 has caused tremendous losses to lives and economy in the entire world. Up until October 2020, it has caused more than 38 million infections and 1.1 million deaths. This has created a severe burden for the health care system worldwide. The machine learning models have been applied to the radiological images of the Covid-19 positive patients for disease prediction and severity assessment. However, a segmentation model for detecting the opacity regions like haziness, ground-glass opacity and lung consolidation from the Covid-19 positive chest X-rays is still lacking. The recently published dataset of a collection of radiological images for a rural population in United States had made development of such a model a possibility due to the high quality of the radiological images and the consistency in clinical measurements. We manually annotated 221 chest X-ray images with lung fields and opacity regions and trained a segmentation model for the opacity region. The model has a good performance in regarding the overlap between predicted and manually labelled opacity regions for both the testing data set and the validation dataset from very different sources. In addition, the percentage of the opacity region over the area of the total lung fields shows a good predictive power for the patient severity. In view of the above, our model is a successful first try in developing a segmentation model for the opacity regions for the Covid-19 positive chest X-rays. However, careful manual examinations of the model predictions by experienced radiologists show mistakenly predicted opacity regions caused probably by the anatomical complexities. Thus, additional work is needed before a robust and accurate model can be developed for the ultimate goal of implementation in the clinical setting. The model, manual segmentation and other supporting materials can be found in https://github.com/haimingt/opacity_segmentation_covid_chest_X_ray.

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Convolutional neural network: a review of models, methodologies and applications to object detection

Cited by 725 publications

References 101 publications

A Computer Vision-Based Dual Network Approach for Indoor Fall Detection

A Computer Vision-Based Dual Network Approach for Indoor Fall Detection

Research of Epidemic Big Data Based on Improved Deep Convolutional Neural Network

Deep learning segmentation model for automated detection of the opacity regions in the chest X-rays of the Covid-19 positive patients and the application for disease severity

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