A review on deep learning approaches in healthcare systems: Taxonomies, challenges, and open issues

Shamshirband, Shahab; Fathi, Mahdis; Dehzangi, Abdollah; Chronopoulos, Anthony Theodore; Alinejad‐Rokny, Hamid

doi:10.1016/j.jbi.2020.103627

“…The bio-medical datasets are going to be bigger and bigger. In the analysis of such data, application of machine learning and deep learning techniques has become more attractive given the rising complexity of the data [42][43][44][45][46]. Therefore, it is important to implement novel techniques to uncover the biomedical patterns, in particular biomedical imaging data.…”

Section: Discussionmentioning

confidence: 99%

CovidMulti-Net: A Parallel-Dilated Multi Scale Feature Fusion Architecture for the Identification of COVID-19 Cases from Chest X-ray Images

Khan

¹

,

Rahman

²

,

Karim

³

et al. 2021

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The COVID-19 pandemic is an emerging respiratory infectious disease, having a significant impact on the health and life of many people around the world. Therefore, early identification of COVID-19 patients is the fastest way to restrain the spread of the pandemic. However, as the number of cases grows at an alarming pace, most developing countries are now facing a shortage of medical resources and testing kits. Besides, using testing kits to detect COVID-19 cases is a time-consuming, expensive, and cumbersome procedure. Faced with these obstacles, most physicians, researchers, and engineers have advocated for the advancement of computer-aided deep learning models to assist healthcare professionals in quickly and inexpensively recognize COVID-19 cases from chest X-ray (CXR) images. With this motivation, this paper proposes a CovidMulti-Net architecture based on the transfer learning concept to classify COVID-19 cases from normal and other pneumonia cases using three publicly available datasets that include 1341, 1341, and 446 CXR images from healthy samples and 902, 1564, and 1193 CXR images infected with Viral Pneumonia, Bacterial Pneumonia, and COVID-19 diseases. In the proposed framework, features from CXR images are extracted using three well-known pre-trained models, including DenseNet-169, ResNet-50, and VGG-19. The extracted features are then fed into a concatenate layer, making a robust hybrid model. The proposed framework achieved a classification accuracy of 99.4%, 95.2%, and 94.8% for 2-Class, 3-Class, and 4-Class datasets, exceeding all the other state-of-the-art models. These results suggest that the CovidMulti-Net frameworks ability to discriminate individuals with COVID-19 infection from healthy ones and provides the opportunity to be used as a diagnostic model in clinics and hospitals. We also made all the materials publicly accessible for the research community at: https://github.com/saikat15010/CovidMulti-Net-Architecture.git.

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“…The increasing availability of big medical data has made it necessary to use machine learning techniques to uncover hidden healthcare patterns [44][45][46][47] . In particular, deep neural networks have been recently used in healthcare applications 48 . Therefore, the proposed model has a great potential to be applicable on healthcare imaging data analysis.…”

Section: Discussionmentioning

confidence: 99%

Four-Layer ConvNet to Facial Emotion Recognition with Minimal Epochs and the Significance of Data Diversity

Debnath¹,

Reza²,

Rahman³

et al. 2021

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Emotion recognition defined as identifying human emotion and is directly related to different fields such as human-computer interfaces, human emotional processing, irrational analysis, medical diagnostics, data-driven animation, human-robot communi- cation and many more. The purpose of this study is to propose a new facial emotional recognition model using convolutional neural network. Our proposed model, “ConvNet”, detects seven specific emotions from image data including anger, disgust, fear, happiness, neutrality, sadness, and surprise. This research focuses on the model’s training accuracy in a short number of epoch which the authors can develop a real-time schema that can easily fit the model and sense emotions. Furthermore, this work focuses on the mental or emotional stuff of a man or woman using the behavioral aspects. To complete the training of the CNN network model, we use the FER2013 databases, and we test the system’s success by identifying facial expressions in the real-time. ConvNet consists of four layers of convolution together with two fully connected layers. The experimental results show that the ConvNet is able to achieve 96% training accuracy which is much better than current existing models. ConvNet also achieved validation accuracy of 65% to 70% (considering different datasets used for experiments), resulting in a higher classification accuracy compared to other existing models. We also made all the materials publicly accessible for the research community at: https://github.com/Tanoy004/Emotion-recognition-through-CNN.

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“…The increasing availability of big medical data has made it necessary to use machine learning techniques to uncover hidden healthcare patterns [44][45][46] . In particular, deep neural networks have been recently used in healthcare applications 47 . Therefore, the proposed model has a great potential to be applicable on healthcare imaging data analysis.…”

Section: Authors Contributionsmentioning

confidence: 99%

Four-layer Convnet to Facial Emotion Recognition With Minimal Epochs and the Significance of Data Diversity

Debnath

¹

,

Reza

²

,

Rahman

³

et al. 2021

Preprint

Self Cite

3

0

View full text Add to dashboard Cite

Emotion recognition defined as identifying human emotion and is directly related to different fields such as human-computer interfaces, human emotional processing, irrational analysis, medical diagnostics, data-driven animation, human-robot communi- cation and many more. The purpose of this study is to propose a new facial emotional recognition model using convolutional neural network. Our proposed model, “ConvNet”, detects seven specific emotions from image data including anger, disgust, fear, happiness, neutrality, sadness, and surprise. This research focuses on the model’s training accuracy in a short number of epoch which the authors can develop a real-time schema that can easily fit the model and sense emotions. Furthermore, this work focuses on the mental or emotional stuff of a man or woman using the behavioral aspects. To complete the training of the CNN network model, we use the FER2013 databases, and we test the system’s success by identifying facial expressions in the real-time. ConvNet consists of four layers of convolution together with two fully connected layers. The experimental results show that the ConvNet is able to achieve 96% training accuracy which is much better than current existing models. ConvNet also achieved validation accuracy of 65% to 70% (considering different datasets used for experiments), resulting in a higher classification accuracy compared to other existing models. We also made all the materials publicly accessible for the research community at: https://github.com/Tanoy004/Emotion-recognition-through-CNN.

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A review on deep learning approaches in healthcare systems: Taxonomies, challenges, and open issues

Cited by 211 publications

References 60 publications

CovidMulti-Net: A Parallel-Dilated Multi Scale Feature Fusion Architecture for the Identification of COVID-19 Cases from Chest X-ray Images

CovidMulti-Net: A Parallel-Dilated Multi Scale Feature Fusion Architecture for the Identification of COVID-19 Cases from Chest X-ray Images

Four-Layer ConvNet to Facial Emotion Recognition with Minimal Epochs and the Significance of Data Diversity

Four-layer Convnet to Facial Emotion Recognition With Minimal Epochs and the Significance of Data Diversity

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