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Earlier Detection of Alzheimer’s Disease Using 3D-Convolutional Neural Networks

Computer Systems Science & Engineering 2023年第8期46卷 2601-2618页

作者： V.P.Nithya N.Mohanasundaram R.Santhosh Department of Computer Science and Engineering Karpagam Academy of Higher EducationCoimbatoreIndia

The prediction of mild cognitive impairment or Alzheimer’s disease(AD)has gained the attention of huge researchers as the disease occurrence is increasing,and there is a need for earlier ***,due to the highdimensionality nature of neural data and the least available samples,modelling an efficient computer diagnostic system is highly *** approaches,specifically deep learning approaches,are essential in disease *** Learning(DL)approaches are successfully demonstrated for their higher-level performance in various fields like medical imaging.A novel 3D-Convolutional Neural Network(3D-CNN)architecture is proposed to predict AD with Magnetic resonance imaging(MRI)*** proposed model predicts the AD occurrence while the existing approaches lack prediction accuracy and perform binary *** proposed prediction model is validated using the Alzheimer’s disease Neuro-Imaging Initiative(ADNI)*** outcomes demonstrate that the anticipated model attains superior prediction accuracy and works better than the brain-image dataset’s general *** predicted model reduces the human effort during the prediction process and makes it easier to diagnose it intelligently as the feature learning is ***’experimentation is carried out,and the model’s superiority is compared with various advanced approaches for multi-level *** proposed model gives better prediction accuracy,precision,recall,and F-measure than other systems like Long Short Term Memory-Recurrent Neural Networks(LSTM-RNN),Stacked Autoencoder with Deep Neural Networks(SAE-DNN),Deep Convolutional Neural Networks(D-CNN),Two Dimensional Convolutional Neural Networks(2D-CNN),Inception-V4,ResNet,and Two Dimensional Convolutional Neural Networks(3D-CNN).

关键词： Alzheimer’s disease 3D CNN ADNI prediction accuracy highdimensionality data

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