2018 · 53 citations · 3 references
MedicineComputer ScienceNeuroscienceComputer VisionDementiaMachine LearningAlzheimer DiseaseBrain ImagingDeep LearningDementia DiagnosisMagnetic Resonance ImagingNeurodegenerative DiseasesConvolutional Neural Network DetectionConvolutional Neural NetworksDeep Learning DetectionEarly Detection Of DementiaMachine IntelligenceMachine Learning In NeuroimagingMultilayer PerceptronStructural Mri
As reported by the the Alzheimer's Association, there are more than 5 million Americans living with Alzheimer's today, with an anticipated 16 million by 2050. The neurodegenerative disease is currently the 6th leading source of death in the US. In 2017 this disease would cost the nation $1.1 trillion. 1 in 3 seniors die in Alzheimer's disease or another dementia. It kills more than breast cancer and prostate cancer combined. [14] As of the this papers writing, detecting Alzheimer's is a difficult and time consuming task, but requires brain imaging report and human expertise. Needless to say, this conventional approach to detect Alzheimer's is costly and often error prone. In this paper an alternative approach has been discussed, that is fast, costs less and more reliable. Deep Learning represents the true bleeding edge of Machine Intelligence. Convolutional Neural Networks are biologically inspired Multilayer perceptron specially capable of image processing. In this paper we present a state of the art Deep Convolutional Neural Network to detect Alzheimer's Disease and Dementia from 3D MRI image.
3
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky et al. · 2014 · 34.3K citations
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Jeff Donahue, Yangqing Jia, Oriol Vinyals et al. · arXiv (Cornell University) · 2013 · 3.6K citations · Full text
Deep Bayesian Active Learning with Image Data
Yarin Gal, Riashat Islam, Zoubin Ghahramani · arXiv (Cornell University) · 2017 · 580 citations · Full text