Shallow 3D CNN for Detecting Acute Brain Hemorrhage From Medical Imaging Sensors

Singh, Satya P.; Wang, Lipo; Gupta, Sukrit; Gulyás, Balázs; Padmanabhan, Parasuraman

doi:10.1109/jsen.2020.3023471

Cited by 86 publications

(24 citation statements)

References 39 publications

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“…For example, in the case of diagnosing the fractures, the study of the deep learning-based classification of fractured regions was implemented for many areas of the human body (e.g., hips, humerus, ribs) and published [23][24][25]. In particular, deep learning models based on 3D-CNN are significant for CAD systems that utilize images to diagnose in medical fields around the world when trying to understand the three-dimensional human body [26,27]. However, the development of an AI-based automatic system using 3D voxel data extracted from the original CT images is less than that of studies using 2D input images.…”

Section: Discussionmentioning

confidence: 99%

A Study on 3D Deep Learning-Based Automatic Diagnosis of Nasal Fractures

Seol

Kim

et al. 2022

Sensors

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This paper reported a study on the 3-dimensional deep-learning-based automatic diagnosis of nasal fractures. (1) Background: The nasal bone is the most protuberant feature of the face; therefore, it is highly vulnerable to facial trauma and its fractures are known as the most common facial fractures worldwide. In addition, its adhesion causes rapid deformation, so a clear diagnosis is needed early after fracture onset. (2) Methods: The collected computed tomography images were reconstructed to isotropic voxel data including the whole region of the nasal bone, which are represented in a fixed cubic volume. The configured 3-dimensional input data were then automatically classified by the deep learning of residual neural networks (3D-ResNet34 and ResNet50) with the spatial context information using a single network, whose performance was evaluated by 5-fold cross-validation. (3) Results: The classification of nasal fractures with simple 3D-ResNet34 and ResNet50 networks achieved areas under the receiver operating characteristic curve of 94.5% and 93.4% for binary classification, respectively, both indicating unprecedented high performance in the task. (4) Conclusions: In this paper, it is presented the possibility of automatic nasal bone fracture diagnosis using a 3-dimensional Resnet-based single classification network and it will improve the diagnostic environment with future research.

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Section: Discussionmentioning

confidence: 99%

A Study on 3D Deep Learning-Based Automatic Diagnosis of Nasal Fractures

Seol

Kim

et al. 2022

Sensors

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“…In recent years, several research efforts have been made for the classification of AD using machine learning [48][49][50]. In particular, most of these machine learning approaches looked at the potential of feature extraction using MRI and diffusion tensor imaging [51].…”

Section: Discussionmentioning

confidence: 99%

Early Detection of Alzheimer’s Disease Using Polar Harmonic Transforms and Optimized Wavelet Neural Network

et al. 2021

Self Cite

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Effective and accurate diagnosis of Alzheimer’s disease (AD), as well as early-stage detection, has gained more and more attention in recent years. For AD classification, we propose a new hybrid method for early detection of Alzheimer’s disease (AD) using Polar Harmonic Transforms (PHT) and Self-adaptive Differential Evolution Wavelet Neural Network (SaDE-WNN). The orthogonal moments are used for feature extraction from the grey matter tissues of structural Magnetic Resonance Imaging (MRI) data. Irrelevant features are removed by the feature selection process through evaluating the in-class and among-class variance. In recent years, WNNs have gained attention in classification tasks; however, they suffer from the problem of initial parameter tuning, parameter setting. We proposed a WNN with the self-adaptation technique for controlling the Differential Evolution (DE) parameters, i.e., the mutation scale factor (F) and the cross-over rate (CR). Experimental results on the Alzheimer’s disease Neuroimaging Initiative (ADNI) database indicate that the proposed method yields the best overall classification results between AD and mild cognitive impairment (MCI) (93.7% accuracy, 86.0% sensitivity, 98.0% specificity, and 0.97 area under the curve (AUC)), MCI and healthy control (HC) (92.9% accuracy, 95.2% sensitivity, 88.9% specificity, and 0.98 AUC), and AD and HC (94.4% accuracy, 88.7% sensitivity, 98.9% specificity and 0.99 AUC).

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“…In [13], a plain and residual 3D CNN is developed to diagnose Alzheimers disease from MRI scans. Singh et al [26] developed a shallow 3D CNN for detecting acute brain hemorrhage from CT scans. Training 3D CNNs is challenging due to stability issues.…”

Section: B Related Workmentioning

confidence: 99%

Brain Attention Regularized Networks: Infection Diagnosis in Hydrocephalus CT Images

Yu¹,

Peterson²,

Cherukuri³

et al. 2022

Preprint

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<div>We investigate the problem of infection diagnosis in hydrocephalic images. While traditional deep learning methods can be used for this problem - they require generous training data and produce class activations that are not meaningful. A novel 2D brain attention regularized network (BAR-Net) is proposed in our work, which encourages the network to focus inside the brain region for classification decisions. The newly proposed regularizer yields more interpretable class activations and experimental results confirm that the issue of over-fitting is mitigated. Extending the 2D BAR-Net, a hybrid 2-D/3-D CNN is proposed, that facilitates interactions between the 2D and 3D branches. Furthermore, a mutual attention regularization term is introduced in order to enable branches to exchange knowledge learned from the other by transferring attention maps for regularization.</div>

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Shallow 3D CNN for Detecting Acute Brain Hemorrhage From Medical Imaging Sensors

Cited by 86 publications

References 39 publications

A Study on 3D Deep Learning-Based Automatic Diagnosis of Nasal Fractures

A Study on 3D Deep Learning-Based Automatic Diagnosis of Nasal Fractures

Early Detection of Alzheimer’s Disease Using Polar Harmonic Transforms and Optimized Wavelet Neural Network

Brain Attention Regularized Networks: Infection Diagnosis in Hydrocephalus CT Images

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