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Prediction of Parkinson Disease using Autoencoder Convolutional Neural.pptx

  1. PREDICTION OF PARKINSON DISEASE USING AUTOENCODER CONVOLUTIONAL NEURAL NETWORKS Presented by -Hema M S -Maheshprabhu R -Prema Arokia Mary G -Nageswara Guptha M -Aditi Sharma
  2. CONTENT • Introduction • Problem Statement • Solution • Methdodology • Conclusion 20XX 2
  3. INTRODUCTION A disorder of the central nervous system that affects movement, often including tremors. Nerve cell damage in the brain causes dopamine levels to drop, leading to the symptoms of Parkinson's. 20XX 3
  5. SOLUTION 5  The predictions of PD in the early stage is important to improve the PD patients’ quality of life.  It can be done using a CNN with autoencoder feature extraction methodology. Autoencoders CNN Encoding Decoding Convolution Layer Pooling Layer Dense Layer
  6.  The ReLU non-linear activation function was used in autoencoder.  The cross entropy was used for error calculation.  The Softmax function was used in the classification layer for classification.  Convolutional neural network was proposed to classify PD patients using vocal data.  The speech recording dataset from Oxford Parkinson diagnostic dataset was taken for experimentation  VGFR spectrogram detector and voice impairment classifier was used to classify PD patients. From dataset using PCA From vocal dataset using stacked autoencoder
  7. MACHINE LEARNING METHODS  Recurrent Neural Network (RNN) based autoencoder such as long- short term unit based autoencoder and gated recurrent based autoencoder was used  Grey wolf optimization algorithm and meta heuristic global search optimization technique and autoencoder were proposed the extract features from PD dataset  Joint kernel based feature selection method was employed to select the features from PD  PD patients was classified using Max-Margin classification. SVM with Gaussian kernel methodology was used for classification  Artificial Neural Network, classification and regression tree and SVM algorithms were implemented for classification  A sparse feature selection was used to select the features from the PD dataset.  Fisher’s linear discriminant analysis, locality preserving projection and least square recursion model were combined  PD was predicted using latent information extraction technique. The latent information was extracted from deep neural network that is CNN  The results showed that logistic regression multi-class classifier produced better results when compared to other 7
  8. METHODOLOGY The goal of the proposed architecture is to predict PD patients from the healthy individuals.  Data Collection  Preprocessing  Feature Extraction  Classification 20XX 8 Input Data pre- processing Encode Decode Feature Extraction – Autoencoder Conv. Layer Pooling Layer Dense layer PD patients / Healthy People Classification - CNN Input Data pre- processing
  9. RESULT Split SVM CNN CNN – AE (proposed) 0.10 0.8234 0.9221 0.9434 0.15 0.8245 0.9389 0.9623 0.20 0.8211 0.9356 0.9444 0.30 0.8067 0.9241 0.9211 I. ACCURACY COMPARISON  The dataset is taken from the PPMI database.
  10. PRECISION COMPARISON OF ALL MODELS Split SVM CNN CNN – AE (proposed) 0.10 0.7590 0.8601 0.9001 0.15 0.7680 0.8931 0.9143 0.20 0.7511 0.8723 0.9023 0.30 0.7423 0.8323 0.8921
  11. I. RECALL COMPARISON OF ALL MODELS Split SVM CNN CNN – AE (proposed) 0.10 0.7790 0.8801 0.9212 0.15 0.7810 0.8923 0.9363 0.20 0.7711 0.8790 0.9210 0.30 0.7412 0.8523 0.9112 SAMPLE FOOTER TEXT 20XX 11 I. MEASURE OF ALL MODELS Split SVM CNN CNN – AE (proposed) 0.10 0.7680 0.8712 0.8934 0.15 0.7789 0.8831 0.9012 0.20 0.7681 0.8612 0.8901 0.30 0.7322 0.8412 0.8823
  12. CONCLUSION  The prediction of PD patients using CNN with autoencoder feature selection is proposed and implemented.  The autoencoder extracted essential features and noise is eliminated from the data.  The convolutional neural network is used for classification.  The CNN segmented the image and extracted the features automatically.  The image is downsized using pooling layer.  The classification is done in the dense layer of CNN. The PPMI data set taken for experimentation.  The accuracy, precision, recall and f1-measured is considered for performance assessment.  The result showed that the proposed methodology performed better when compared to other methodologies.