Modelling a Deep Learning Framework for Recognition of Human Actions on Video

2021 ◽  
pp. 104-112
Author(s):  
Flávio Santos ◽  
Dalila Durães ◽  
Francisco Marcondes ◽  
Marco Gomes ◽  
Filipe Gonçalves ◽  
...  
Keyword(s):  
Deep Learning ◽  
Human Actions ◽  
Learning Framework

scholarly journals DISTRIBUTED DEEP LEARNING FRAMEWORK FOR SMART BUILDING TRANSDUCER NETWORK

2021 ◽  
Vol 4 (2) ◽  
pp. 127-139
Author(s):  
Ivan M. Lobachev ◽  
Svitlana G. Antoshchuk ◽  
Mykola A. Hodovychenko
Keyword(s):  
Deep Learning ◽  
Operating Time ◽  
Third Party ◽  
Process Data ◽  
Moderate Amount ◽  
Human Actions ◽  
Learning Framework ◽  
Distributed Framework ◽  
Work Variations ◽  
Hardware Platforms

This work is devoted to the development of a distributed framework based on deep learning for processing data from various sensors that are generated by transducer networks that are used in the field of smart buildings. The proposed framework allows you to process data that comes from sensors of various types to solve classification and regression problems. The framework architecture consists of several subnets: particular convolutional net that handle input from the same type of sensors, a single convolutional fusion net that processes multiple outputs of particular convolutional nets. Further, the result of a single convolutional fusion net is fed to the input of a recurrent net, which allows extracting meaningful features from time sequences. The result of the recurrent net opera- tion is fed to the output layer, which generates the framework output based on the type of problem being solved. For the experimental evaluation of the developed framework, two tasks were taken: the task of recognizing human actions and the task of identifying a person by movement. The dataset contained data from two sensors (accelerometer and gyroscope), which were collected from 9 users who performed 6 actions. A mobile device was used as the hardware platforms, as well as the Edison Compute Module hardware device. To compare the results of the work, variations of the proposed framework with different architectures were used, as well as third-party approaches based on various methods of machine learning, including support machines of vectors, a random forest, lim- ited Boltzmann machines, and so on. As a result, the proposed framework, on average, surpassed other algorithms by about 8% in three metrics in the task of recognizing human actions and turned out to be about 13% more efficient in the task of identifying a per- son by movement. We also measured the power consumption and operating time of the proposed framework and its analogues. It was found that the proposed framework consumes a moderate amount of energy, and the operating time can be estimated as acceptable.

A Deep Learning Framework for Prediction of Retinal Disorders

2020 ◽  
Author(s):  
Raniyaharini R ◽  
Madhumitha K ◽  
Mishaa S ◽  
Virajaravi R
Keyword(s):  
Deep Learning ◽  
Learning Framework ◽  
Retinal Disorders

COVID-19 pneumonia diagnosis using a simple 2D deep learning framework with a single chest CT image (Preprint)

2020 ◽  
Author(s):  
Jinseok Lee
Keyword(s):  
Deep Learning ◽  
Diagnostic Performance ◽  
Ct Images ◽  
Chest Ct ◽  
University Hospital ◽  
Detection Accuracy ◽  
Ct Image ◽  
Test Dataset ◽  
Learning Framework ◽  
Testing Dataset

BACKGROUND The coronavirus disease (COVID-19) has explosively spread worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) can be used as a relevant screening tool owing to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely busy fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians. OBJECTIVE We aimed to quickly develop an AI technique to diagnose COVID-19 pneumonia and differentiate it from non-COVID pneumonia and non-pneumonia diseases on CT. METHODS A simple 2D deep learning framework, named fast-track COVID-19 classification network (FCONet), was developed to diagnose COVID-19 pneumonia based on a single chest CT image. FCONet was developed by transfer learning, using one of the four state-of-art pre-trained deep learning models (VGG16, ResNet50, InceptionV3, or Xception) as a backbone. For training and testing of FCONet, we collected 3,993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and non-pneumonia diseases from Wonkwang University Hospital, Chonnam National University Hospital, and the Italian Society of Medical and Interventional Radiology public database. These CT images were split into a training and a testing set at a ratio of 8:2. For the test dataset, the diagnostic performance to diagnose COVID-19 pneumonia was compared among the four pre-trained FCONet models. In addition, we tested the FCONet models on an additional external testing dataset extracted from the embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers. RESULTS Of the four pre-trained models of FCONet, the ResNet50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100%, and accuracy 99.87%) and outperformed the other three pre-trained models in testing dataset. In additional external test dataset using low-quality CT images, the detection accuracy of the ResNet50 model was the highest (96.97%), followed by Xception, InceptionV3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively). CONCLUSIONS The FCONet, a simple 2D deep learning framework based on a single chest CT image, provides excellent diagnostic performance in detecting COVID-19 pneumonia. Based on our testing dataset, the ResNet50-based FCONet might be the best model, as it outperformed other FCONet models based on VGG16, Xception, and InceptionV3.

scholarly journals Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Xiaodong Wang ◽  
Ying Chen ◽  
Yunshu Gao ◽  
Huiqing Zhang ◽  
Zehui Guan ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Deep Learning ◽  
Lymph Node ◽  
Lymph Nodes ◽  
Main Method ◽  
Learning Framework ◽  
Prognostic Assessment ◽  
N Stage ◽  
Cancer Outcome ◽  
Whole Slide Images

AbstractN-staging is a determining factor for prognostic assessment and decision-making for stage-based cancer therapeutic strategies. Visual inspection of whole-slides of intact lymph nodes is currently the main method used by pathologists to calculate the number of metastatic lymph nodes (MLNs). Moreover, even at the same N stage, the outcome of patients varies dramatically. Here, we propose a deep-learning framework for analyzing lymph node whole-slide images (WSIs) to identify lymph nodes and tumor regions, and then to uncover tumor-area-to-MLN-area ratio (T/MLN). After training, our model’s tumor detection performance was comparable to that of experienced pathologists and achieved similar performance on two independent gastric cancer validation cohorts. Further, we demonstrate that T/MLN is an interpretable independent prognostic factor. These findings indicate that deep-learning models could assist not only pathologists in detecting lymph nodes with metastases but also oncologists in exploring new prognostic factors, especially those that are difficult to calculate manually.

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