Journal ArticleDOI
Multi-Class brain normality and abnormality diagnosis using modified Faster R-CNN.
TLDR
In this paper, a ResNet50 modified Faster Regions with Convolutional Neural Network (R-CNN) model was proposed to diagnose brain normality and abnormalities using a novel R-CNN model and the proposed model both determines the borders of the normal and abnormal parts and classifies them with the highest accuracy.About:
This article is published in International Journal of Medical Informatics.The article was published on 2021-11-01. It has received 4 citations till now. The article focuses on the topics: Deep learning & Convolutional neural network.read more
Citations
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Journal ArticleDOI
A novel multivariable time series prediction model for acute kidney injury in general hospitalization
TL;DR: Wang et al. as discussed by the authors built a multivariate time series prediction model for dynamic acute kidney injury (AKI) prediction in general hospitalization, which outperformed models generated by mainstay machine learning methods and most of the published machine learning models.
Journal ArticleDOI
Classification of brain hemorrhage computed tomography images using OzNet hybrid algorithm
TL;DR: In this paper , the authors developed OzNet hybrid algorithm, which is a novel convolution neural networks (CNN) algorithm and combine it with Neighborhood Component Analysis (NCA) and many classifiers: Artificial Neural Networks (ANN), Adaboost, Bagging, Decision Tree, KNN, Linear Discriminant Analysis (LDA), Naïve Bayes and Support Vector Machines (SVM), and Oznet is utilized for feature extraction, where 4096 features are extracted from the fully connected layer.
Journal ArticleDOI
A deep learning model based on dynamic contrast-enhanced magnetic resonance imaging enables accurate prediction of benign and malignant breast lessons
TL;DR: The CNN model based on DCE-MRI demonstrated high accuracy for predicting malignancy among the breast lesions and should be validated in a larger and independent cohort.
Book ChapterDOI
Artificial intelligence-based brain hemorrhage detection
TL;DR: In this article , the authors utilized artificial intelligence for brain hemorrhage detection by using different machine learning and deep learning architectures, which achieved a 96% accuracy with a brain-hemorrhage CT dataset.
References
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Journal ArticleDOI
Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia.
TL;DR: The proposed schemes and reported findings attained by using the DNN classifier and whole-brain FC data suggest that such approaches show improved ability to learn hidden patterns in brain imaging data, which may be useful for developing diagnostic tools for SZ and other neuropsychiatric disorders and identifying associated aberrant FC patterns.
Journal ArticleDOI
Accuracy of dementia diagnosis - a direct comparison between radiologists and a computerized method
Stefan Klöppel,Cynthia M. Stonnington,Josephine Barnes,F Chen,Carlton Chu,Catriona D. Good,Irina Mader,L.A. Mitchell,L.A. Mitchell,A.C. Patel,Catherine C. Roberts,Nick C. Fox,Clifford R. Jack,John Ashburner,Rsj Frackowiak,Rsj Frackowiak +15 more
TL;DR: The results show that well-trained neuroradiologists classify typical Alzheimer's disease-associated scans comparable to SVMs, and indicate a role for computerized diagnostic methods in clinical practice.
Proceedings ArticleDOI
RADnet: Radiologist level accuracy using deep learning for hemorrhage detection in CT scans
TL;DR: In this article, a deep learning approach for automated brain hemorrhage detection from computed tomography (CT) scans is described, which employs original DenseNet architecture along with adding the components of attention for slice level predictions and recurrent neural network layer for incorporating 3D context.
Journal ArticleDOI
The need to separate the wheat from the chaff in medical informatics: Introducing a comprehensive checklist for the (self)-assessment of medical AI studies
TL;DR: In this paper, the authors propose a practical checklist to help authors to self assess the quality of their contribution and to help reviewers to recognize and appreciate high-quality medical ML studies by distinguishing them from the mere application of ML techniques to medical data.
Journal ArticleDOI
3D-CNN based discrimination of schizophrenia using resting-state fMRI.
TL;DR: These ICA functional network maps served as highly discriminative three-dimensional imaging features for the discrimination of schizophrenia in this study and may be translated in future as an adjunct tool to assist clinicians in the initial screening of schizophrenia.