A comparison of pre-trained CNNs on noisy X-ray fracture detection finds VGG16 far more robust than ResNet50 or EfficientNet, suggesting a complexity-robustness tradeoff.
A Convolutional-based Model for Early Prediction of Alzheimer's based on the Dementia Stage in the MRI Brain Images
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abstract
Alzheimer's disease is a degenerative brain disease. Being the primary cause of Dementia in adults and progressively destroys brain memory. Though Alzheimer's disease does not have a cure currently, diagnosing it at an earlier stage will help reduce the severity of the disease. Thus, early diagnosis of Alzheimer's could help to reduce or stop the disease from progressing. In this paper, we proposed a deep convolutional neural network-based model for learning model using to determine the stage of Dementia in adults based on the Magnetic Resonance Imaging (MRI) images to detect the early onset of Alzheimer's.
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Pre-trained Under Noise: A Framework for Robust Bone Fracture Detection in Medical Imaging
A comparison of pre-trained CNNs on noisy X-ray fracture detection finds VGG16 far more robust than ResNet50 or EfficientNet, suggesting a complexity-robustness tradeoff.