Alcoholism Identification Based on an AlexNet Transfer Learning Model

Wang, Shuihua; Xie, Sihong; Chen, Xianqing; Guttery, David S.; Tang, Chaosheng; Sun, Jianfang; Zhang, Yudong

doi:10.3389/fpsyt.2019.00205

Cited by 93 publications

(51 citation statements)

References 38 publications

(49 reference statements)

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“…DL solutions have been applied to medical image analysis [120] in very different topics related to classification or detection, such as identifying lung nodules into benign or malignant [121], alcoholism identification through brain MRI [122,123], multiple sclerosis detection [124], categorizing chest X-rays into different diseases [125], distinguishing patients with Alzheimer's disease versus normal [126], diagnosing diabetic retinopathy using digital photographs of the fundus of the eye [127], discriminating kidney cancer histopathological images into tumor or non-tumor [128], detecting cancerous lung nodules [129] and lung cancer stage [130] on CT scans, malignant skin cells on dermatological photographs [131], mitotic figures in breast histology images [132], or cell nuclei in colorectal adenocarcinoma histology image [133]. Regarding image segmentation, DL covers a variety of organs such as liver, prostate, spine, and knee cartilage both in CT and MRI [116,134].…”

Section: Biomedical Imagesmentioning

confidence: 99%

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

et al. 2019

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In the last few years, there has been a growing expectation created about the analysis of large amounts of data often available in organizations, which has been both scrutinized by the academic world and successfully exploited by industry. Nowadays, two of the most common terms heard in scientific circles are Big Data and Deep Learning. In this double review, we aim to shed some light on the current state of these different, yet somehow related branches of Data Science, in order to understand the current state and future evolution within the healthcare area. We start by giving a simple description of the technical elements of Big Data technologies, as well as an overview of the elements of Deep Learning techniques, according to their usual description in scientific literature. Then, we pay attention to the application fields that can be said to have delivered relevant real-world success stories, with emphasis on examples from large technology companies and financial institutions, among others. The academic effort that has been put into bringing these technologies to the healthcare sector are then summarized and analyzed from a twofold view as follows: first, the landscape of application examples is globally scrutinized according to the varying nature of medical data, including the data forms in electronic health recordings, medical time signals, and medical images; second, a specific application field is given special attention, in particular the electrocardiographic signal analysis, where a number of works have been published in the last two years. A set of toy application examples are provided with the publicly-available MIMIC dataset, aiming to help the beginners start with some principled, basic, and structured material and available code. Critical discussion is provided for current and forthcoming challenges on the use of both sets of techniques in our future healthcare.

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Section: Biomedical Imagesmentioning

confidence: 99%

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

et al. 2019

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“…The RMSEs of CNN transfer learning can be smaller because the transfer-learning approach in this study was simple (i.e., retrainings only of the fully connected layers 1 and 2). In general, if some deeper layers close to the end of the CNN is retrained, the transfer learning can be more effective for improving predictions (e.g., Wang et al [34]). For example, we may perfume the sensitivity tests of retraining the layers, such as the max-pooling 2 and the fully connected layers 1 and 2 (Figure 5a).…”

Section: Discussionmentioning

confidence: 99%

Convolutional Neural Network Coupled with a Transfer-Learning Approach for Time-Series Flood Predictions

Kimura

Yoshinaga

Sekijima

et al. 2019

Water

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East Asian regions in the North Pacific have recently experienced severe riverine flood disasters. State-of-the-art neural networks are currently utilized as a quick-response flood model. Neural networks typically require ample time in the training process because of the use of numerous datasets. To reduce the computational costs, we introduced a transfer-learning approach to a neural-network-based flood model. For a concept of transfer leaning, once the model is pretrained in a source domain with large datasets, it can be reused in other target domains. After retraining parts of the model with the target domain datasets, the training time can be reduced due to reuse. A convolutional neural network (CNN) was employed because the CNN with transfer learning has numerous successful applications in two-dimensional image classification. However, our flood model predicts time-series variables (e.g., water level). The CNN with transfer learning requires a conversion tool from time-series datasets to image datasets in preprocessing. First, the CNN time-series classification was verified in the source domain with less than 10% errors for the variation in water level. Second, the CNN with transfer learning in the target domain efficiently reduced the training time by 1/5 of and a mean error difference by 15% of those obtained by the CNN without transfer learning, respectively. Our method can provide another novel flood model in addition to physical-based models.

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“…Furthermore, the joint moment is not easily measured in real time. Previous studies [23][24][25][26] indicated that this challenge may be addressed by using the artificial neural network (ANN) model, because of its excellent adaptive ability to individual characteristics [27,28]. For example, Uchiyama et al [29], used an ANN model to predict the elbow joint moment with the inputs of EMG signals, elbow and shoulder joint angles, while Luh et al [30], and Song and Tong [31] utilized an ANN model with EMG signals, elbow joint angle and angular velocity for the same purpose.…”

Section: Introductionmentioning

confidence: 99%

Determining the Online Measurable Input Variables in Human Joint Moment Intelligent Prediction Based on the Hill Muscle Model

Xiong

Zeng

et al. 2020

Sensors

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Introduction: Human joint moment is a critical parameter to rehabilitation assessment and human-robot interaction, which can be predicted using an artificial neural network (ANN) model. However, challenge remains as lack of an effective approach to determining the input variables for the ANN model in joint moment prediction, which determines the number of input sensors and the complexity of prediction. Methods: To address this research gap, this study develops a mathematical model based on the Hill muscle model to determining the online input variables of the ANN for the prediction of joint moments. In this method, the muscle activation, muscle-tendon moment velocity and length in the Hill muscle model and muscle-tendon moment arm are translated to the online measurable variables, i.e., muscle electromyography (EMG), joint angles and angular velocities of the muscle span. To test the predictive ability of these input variables, an ANN model is designed and trained to predict joint moments. The ANN model with the online measurable input variables is tested on the experimental data collected from ten healthy subjects running with the speeds of 2, 3, 4 and 5 m/s on a treadmill. The variance accounted for (VAF) between the predicted and inverse dynamics moment is used to evaluate the prediction accuracy. Results: The results suggested that the method can predict joint moments with a higher accuracy (mean VAF = 89.67±5.56 %) than those obtained by using other joint angles and angular velocities as inputs (mean VAF = 86.27±6.6%) evaluated by jack-knife cross-validation. Conclusions: The proposed method provides us with a powerful tool to predict joint moment based on online measurable variables, which establishes the theoretical basis for optimizing the input sensors and detection complexity of the prediction system. It may facilitate the research on exoskeleton robot control and real-time gait analysis in motor rehabilitation.

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Alcoholism Identification Based on an AlexNet Transfer Learning Model

Cited by 93 publications

References 38 publications

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

Convolutional Neural Network Coupled with a Transfer-Learning Approach for Time-Series Flood Predictions

Determining the Online Measurable Input Variables in Human Joint Moment Intelligent Prediction Based on the Hill Muscle Model

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