Deep Convolutional Neural Networks for Classifying Body Constitution Based on Face Image

Huan, Er-Yang; Wen, Gongjian; Zhang, Shijun; Li, Danyang; Hu, Yang; Chang, Tianyuan; Wang, Qing; Huang, Bing-Lin

doi:10.1155/2017/9846707

Cited by 31 publications

(20 citation statements)

References 37 publications

(30 reference statements)

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“…Er-Yang Huan et al [12] suggest a body constitution recognition procedure grounded on CNN that can recognize person constitution kinds based to face pictures. The determined model first utilizes CNN to extract the face picture attributes and then fused the abstracted features with the hue attributes.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Feature Extraction for Brain Tumor Characterization and Detection

Basir

Shantta

2021

IRA-JAS

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Deep Learning is a growing field of artificial intelligence that has become an operative research topic in a wide range of disciplines. Today we are witnessing the tangible successes of Deep Learning in our daily lives in various applications, including education, manufacturing, transportation, healthcare, military, and automotive, etc.<strong> </strong>Deep Learning is a subfield of Machine Learning that stems from Artificial Neural Networks, where a cascade of layers is employed to progressively extract higher-level features from the raw input and make predictive guesses about new data. This paper will discuss the effect of attribute extraction profoundly inherent in training<strong> </strong>approaches such as Convolutional Neural Networks (CNN). Furthermore, the paper aims to offer a study on Deep Learning techniques and attribute extraction methods that have appeared in the last few years. As the demand increases, considerable research in the attribute extraction assignment has become even more instrumental. Brain tumor characterization and detection will be used as a case study to demonstrate Deep Learning CNN's ability to achieve effective representational learning and tumor characterization.

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Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Feature Extraction for Brain Tumor Characterization and Detection

Basir

Shantta

2021

IRA-JAS

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“…Ma et al 27 proposed a framework based on deep CNN architecture to recognize the BC types according to tongue images. Besides, Huan et al 28 use deep CNNs to classify BC types according to face images. In wrist pulse signal recognition, Hu et al 29 achieved a significant performance improvement by using a trained deep CNN model.…”

Section: Related Workmentioning

confidence: 99%

A multimodal deep architecture for traditional Chinese medicine diagnosis

Dai

Wang

Dai

et al. 2020

Concurrency and Computation

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Traditional Chinese medicine (TCM) is found on a long‐term medical practice in China. Rare human brains can fully grasp the deep TCM knowledge derived from a tremendous amount of experience. In this big data era, a big electronic brain might be competent via deep learning techniques. For this prospect, the electronic brain needs to process various heterogeneous data, such as images, texts, audio signals, and other sensory data. It used to be a challenge to analyze the heterogeneous data by the computer‐aided system until the advances of the powerful deep learning tools. We propose a multimodal deep learning framework to mimic a TCM practitioner to diagnose a patient on the basis of multimodal perceptions of see, listen, smell, ask, and touch. The framework learns common representations from various high‐dimensional sensory data, and fuse the information for final classification. We propose to use conceptual alignment deep neural networks to embed prior knowledge and obtain interpretable latent representations. We implement a multimodal deep architecture to process tongue image and description text data for TCM diagnosis. Experiments illustrate that the multimodal deep architecture can extract effective features from heterogeneous data, produce interpretable representations, and finally achieve a higher accuracy than either corresponding unimodal architectures.

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“…Recently, researchers proposed a deep learning model named convolutional neural network (CNN), reduces the complexity of the network and number of weights because of its shared-weight network structure. It is being extensively deployed in the domain of object recognition [20] as well as image segmentation [21,22].…”

Section: Introductionmentioning

confidence: 99%

CNN-Based Smart Sleep Posture Recognition System

Tang

Kumar

Nadeem

et al. 2021

IoT

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Sleep pattern and posture recognition have become of great interest for a diverse range of clinical applications. Autonomous and constant monitoring of sleep postures provides useful information for reducing the health risk. Prevailing systems are designed based on electrocardiograms, cameras, and pressure sensors, which are not only expensive but also intrusive in nature, and uncomfortable to use. We propose an unobtrusive and affordable smart system based on an electronic mat called Sleep Mat-e for monitoring the sleep activity and sleep posture of individuals living in residential care facilities. The system uses a pressure sensing mat constructed using piezo-resistive material to be placed on a mattress. The sensors detect the distribution of the body pressure on the mat during sleep and we use convolution neural network (CNN) to analyze collected data and recognize different sleeping postures. The system is capable of recognizing the four major postures—face-up, face-down, right lateral, and left lateral. A real-time feedback mechanism is also provided through an accompanying smartphone application for keeping a diary of the posture and send alert to the user in case there is a danger of falling from bed. It also produces synopses of postures and activities over a given duration of time. Finally, we conducted experiments to evaluate the accuracy of the prototype, and the proposed system achieved a classification accuracy of around 90%.

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Deep Convolutional Neural Networks for Classifying Body Constitution Based on Face Image

Cited by 31 publications

References 37 publications

Deep Learning Feature Extraction for Brain Tumor Characterization and Detection

Deep Learning Feature Extraction for Brain Tumor Characterization and Detection

A multimodal deep architecture for traditional Chinese medicine diagnosis

CNN-Based Smart Sleep Posture Recognition System

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