ArticleJournal of medical systems2017
Alcoholism Detection by Data Augmentation and Convolutional Neural Network with Stochastic Pooling.
Article in Journal of medical systems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 180 citations in OpenAlex.
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
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- CI-UNet: melding convnext and cross-dimensional attention for robust medical image segmentation.Biomedical engineering letters · 2024Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Stochastic Dilated Residual Ghost Model for Breast Cancer Detection.Journal of digital imaging · 2023Article
- A Survey of Convolutional Neural Network in Breast Cancer.Computer modeling in engineering & sciences : CMES · 2023Article
- Music Similarity Detection Guided by Deep Learning Model.Computational intelligence and neuroscience · 2023Article
- Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation.Frontiers in neuroimaging · 2022Article
- Deep learning in neuroimaging data analysis: Applications, challenges, and solutions.Frontiers in neuroimaging · 2022Review
- Pooling in convolutional neural networks for medical image analysis: a survey and an empirical study.Neural computing & applications · 2022Review
- VISPNN: VGG-inspired Stochastic Pooling Neural Network.Computers, materials & continua · 2022Article
- CNN-based qualitative detection of bone mineral density via diagnostic CT slices for osteoporosis screening.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2021Article
- Deep Learning in the Classification of Stage of Liver Fibrosis in Chronic Hepatitis B with Magnetic Resonance ADC Images.Contrast media & molecular imaging · 2021Article
- Design of the Piano Score Recommendation Image Analysis System Based on the Big Data and Convolutional Neural Network.Computational intelligence and neuroscience · 2021Article
- Predicting Body Mass Index From Structural MRI Brain Images Using a Deep Convolutional Neural Network.Frontiers in neuroinformatics · 2020Article
- Deep neural networks in psychiatry.Molecular psychiatry · 2019Review
- Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations.JMIR mHealth and uHealth · 2019Article
- Alcoholism Identification Based on an AlexNet Transfer Learning Model.Frontiers in psychiatry · 2019Article
- [Artificial intelligence in psychiatry-an overview].Der Nervenarzt · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 6 institutions in 2 countries.
Funding
Abstract
Alcohol use disorder (AUD) is an important brain disease. It alters the brain structure. Recently, scholars tend to use computer vision based techniques to detect AUD. We collected 235 subjects, 114 alcoholic and 121 non-alcoholic. Among the 235 image, 100 images were used as training set, and data augmentation method was used. The rest 135 images were used as test set. Further, we chose the latest powerful technique-convolutional neural network (CNN) based on convolutional layer, rectified linear unit layer, pooling layer, fully connected layer, and softmax layer. We also compared three different pooling techniques: max pooling, average pooling, and stochastic pooling. The results showed that our method achieved a sensitivity of 96.88%, a specificity of 97.18%, and an accuracy of 97.04%. Our method was better than three state-of-the-art approaches. Besides, stochastic pooling performed better than other max pooling and average pooling. We validated CNN with five convolution layers and two fully connected layers performed the best. The GPU yielded a 149× acceleration in training and a 166× acceleration in test, compared to CPU.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.