A Smile Detection Method Based on Improved LeNet-5 and Support Vector Machine

Ma, Miao; Gao, Ziang; Wu, Jie; Chen, Yuli; Zheng, Xiaolong

doi:10.1109/smartworld.2018.00104

Cited by 12 publications

(6 citation statements)

References 12 publications

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“…The feature maps, convolution layer strides and fully-connected layer nodes in the standard LeNet-5 may not be suitable for this scene. Therefore, we adjusted LeNet-5 as follows: (1) using a one-dimensional convolution operation instead of a two-dimensional convolution operation to feature extraction (Kiranyaz, Ince & Gabbouj, 2015); (2) adding a dropout layer between the convolution layer and fully connected layer to avoid over-fitting (Srivastava et al, 2014); (3) retaining only one fully connected layer to reduce network complexity (Ma et al, 2018); (4) modifying the size of the convolution layer strides and the number of fully-connected layer nodes. The architecture and details of our modified LeNet-5 are shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Sleep apnea detection from a single-lead ECG signal with automatic feature-extraction through a modified LeNet-5 convolutional neural network

Wang

Shen

et al. 2019

PeerJ

114

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Sleep apnea (SA) is the most common respiratory sleep disorder, leading to some serious neurological and cardiovascular diseases if left untreated. The diagnosis of SA is traditionally made using Polysomnography (PSG). However, this method requires many electrodes and wires, as well as an expert to monitor the test. Several researchers have proposed instead using a single channel signal for SA diagnosis. Among these options, the ECG signal is one of the most physiologically relevant signals of SA occurrence, and one that can be easily recorded using a wearable device. However, existing ECG signal-based methods mainly use features (i.e. frequency domain, time domain, and other nonlinear features) acquired from ECG and its derived signals in order to construct the model. This requires researchers to have rich experience in ECG, which is not common. A convolutional neural network (CNN) is a kind of deep neural network that can automatically learn effective feature representation from training data and has been successfully applied in many fields. Meanwhile, most studies have not considered the impact of adjacent segments on SA detection. Therefore, in this study, we propose a modified LeNet-5 convolutional neural network with adjacent segments for SA detection. Our experimental results show that our proposed method is useful for SA detection, and achieves better or comparable results when compared with traditional machine learning methods.

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Section: Methodsmentioning

confidence: 99%

Sleep apnea detection from a single-lead ECG signal with automatic feature-extraction through a modified LeNet-5 convolutional neural network

Wang

Shen

et al. 2019

PeerJ

114

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show abstract

“…• Dropout layers were included between the convolution layers and fully connected layers to prevent over-fitting [41]. • For the purpose of achieving a balance between network complexity and performance, only a single fully connected layer was retained, in contrast to the multiple layers found in many deep learning models [42]. • A dropout layer with a rate of 0.5 was introduced between the convolution layers and the fully connected layer, which is different from the 0.8 rate in the prior modified model.…”

Section: Algorithm 1 Algorithm For Determining the Ecg P Peaksmentioning

confidence: 99%

Aquaculture systems in Hanoi, Vietnam.

Nguyen¹

2020

Preprint

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Hanoi, the capital of Vietnam, is one of the most densely populated areas in the world, with about 3,000 people km-2. Traditionally, aquaculture is practiced in ponds, reservoirs and urban lakes, rice fields, wastewater and VAC [an acronym from the Vietnamese words for garden (vuon), pond (ao) and livestock quarters (chuong)] systems. With over 5,000 ha of surface area and 3,000 ha of lowland rice fields, Hanoi has potential for further aquaculture development and tourism-entertainment services. The importance of aquaculture has long been recognized as a source of income and employment. Nowadays, pond aquaculture in peri-urban areas is decreasing due to urbanization, while in areas far from the city it is increasing through the conversion of lowland rice fields to fish ponds.

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“…ResNet proposed by He et al after GoogLeNet also achieved very good results in image recognition and detection tasks, speeding up CNN training speed, but its disadvantage is that the training precision is not high [33]. In addition, if too many parameters are used in network training, it is prone to over tting [34] and affecting the e ciency of the model. GoogLeNet inceptionV1 is the most suitable DL model in this study to assist the automatic classi cation and recognition of vitreous opacity US images, and its classi cation accuracy is as high as 96%.…”

Section: Resultsmentioning

confidence: 99%

Applying Deep learning in Recognizing the Properties of Vitreous Opacity on Ophthalmic Ultrasound Images

Li¹,

Zhang²,

Wei³

et al. 2023

Preprint

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BACKGROUND: To explore the feasibility of artificial intelligence technology based on deep learning to automatically recognize the properties of vitreous opacities in ophthalmic ultrasound images. METHODS: The normal and three typical vitreous opacities confirmed as physiological vitreous opacity (VO), asteroid hyalosis (AH) and vitreous hemorrhage (VH)，were selected and marked from 2000 gray scale Color Doppler ultrasound images for each lesion. Five residual networks (ResNet) and two GoogLeNet models were trained to recognize the vitreous lesions. 75% images were randomly selected as the training set, the remaining 25% as a test set. The accuracy and parameters were recorded and compared among these seven different deep learning (DL) models. The precision, recall, FI score and the area under the receiver operating characteristic curves (AUC) values of recognizing the vitreous lesions were calculated with the most accurate DL model. RESULTS: There were significant statistical differences in the accuracy and parameters among these seven DL models. GoogleNet inception V1 achieved the highest accuracy (95.5%) and the least parameters (10315580) in recognizing the vitreous lesions. GoogleNet inception V1 achieved 0.94, 0.94, 0.96, and 0.96 precision；0.94, 0.93, 0.97and 0.98 recall ；0.94, 0.93, 0.96 and 0.97 F1Score in recognizing normal, VO, AH, and VH. The AUC values of these four vitreous lesions were 0.99, 1.0, 0.99 and 0.99, respectively. CONCLUSIONS: GoogLeNet inception V1 has shown promising results in recognizing the ophthalmic ultrasound image. With more and more ultrasound image data, a wide variety of hidden information in the eye diseases can be clearly detected automatically by the artificial intelligence technology based on deep learning.

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A Smile Detection Method Based on Improved LeNet-5 and Support Vector Machine

Cited by 12 publications

References 12 publications

Sleep apnea detection from a single-lead ECG signal with automatic feature-extraction through a modified LeNet-5 convolutional neural network

Sleep apnea detection from a single-lead ECG signal with automatic feature-extraction through a modified LeNet-5 convolutional neural network

Aquaculture systems in Hanoi, Vietnam.

Applying Deep learning in Recognizing the Properties of Vitreous Opacity on Ophthalmic Ultrasound Images

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