Book ChapterDOI
Machine Learning Based Diagnosis of Alzheimer’s Disease
M. Karthiga,S. Sountharrajan,S. S. Nandhini,B. Sathis Kumar +3 more
- pp 607-619
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TLDR
The proposed model with 95.66% accuracy predicts the AD from the brain MRI images at an earlier stage is far better than other classifiers like Decision Tree and Random Forest.Abstract:
Alzheimer’s disease is the most dreadful. Despite the various Alzheimer’s treatments available nowadays, the survival rate of Alzheimer’s patients is very much low. Detection of Alzheimer’s disease in an earlier stage is one of the most important things to reduce the death rate. Magnetic Resonance Imaging (MRI) is an important tool in medical informatics and clinical diagnosis. This MRI image helps to diagnose and detect Alzheimer’s disease at advanced stages. There are several approaches implemented to discover Alzheimer’s by MRI data. Features are extracted discriminately and combinations of different classification techniques for classification is implemented in the proposed work. In this paper, the curvelet-based transform technique is utilized for extracting the features. The AdaBoost classifier is utilized for combining multiple weak classifiers into one strong classifier to improve the accuracy of the result. To improve the efficiency of the result, AdaBoost classifier with SVM was used to obtain better results than existing ones. The proposed model with 95.66% accuracy predicts the AD from the brain MRI images at an earlier stage. This accuracy value is far better than other classifiers like Decision Tree and Random Forest.read more
Citations
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Journal ArticleDOI
Alzheimer's disease prediction using machine learning techniques and principal component analysis (PCA)
M. Sudharsan,G. Thailambal +1 more
TL;DR: A proposed and related Alzheimer's disease early diagnostic method using Mild Cognitive Impairment, Structural Magnetic Resonance imaging for AD-discrimination and healthy control participants (HC) with Import Vector Machine, Regularized Extreme Learning Machine (RELM) and a Support vector machine (SVM).
Journal ArticleDOI
Alzheimer's disease prediction using machine learning techniques and principal component analysis (PCA)
TL;DR: In this paper , the authors proposed and related Alzheimer's disease early diagnostic method using Mild Cognitive Impairment (MCI), Structural Magnetic Resonance (sMR) imaging for AD-discrimination and healthy control participants (HC) with Import Vector Machine (IVM), Regularized Extreme Learning Machine (RELM) and a Support vector machine (SVM).
Journal ArticleDOI
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
TL;DR: A Hybrid model is proposed, which is a combination of VGG19 with additional layers, and a CNN deep learning model for detecting and classifying the different stages of Alzheimer’s and the performance is compared with the CNN model.
Journal ArticleDOI
Triplet loss for Chromosome Classification
TL;DR: A similarity learning approach is proposed in this paper using ‘Triplet Loss’ for procuring high-dimensional embeddings and delivers a superlative performance when compared to a baseline Convolutional Neural Network (CNN) on a publicly available chromosome classification dataset.
Proceedings ArticleDOI
Prostate Cancer Prognosis-a comparative approach using Machine Learning Techniques
Sagar.C. Bellad,Ananya Mahapatra,Sahil Dilip Ghule,Satvik Sridhar Shetty,S. Sountharrajan,M. Karthiga,E. Suganya +6 more
TL;DR: In this article, the authors focused on the working of various classifiers for prediction of prostate Cancer in calculating the level of efficiency in prediction and this helps in selecting the best method.
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Recovering edges in ill-posed inverse problems: optimality of curvelet frames
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Journal ArticleDOI
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TL;DR: This study suggests that as Alzheimer's disease progresses, subregional hippocampal atrophy spreads in a pattern that follows the known trajectory of neurofibrillary tangle dissemination.