Alzheimer’s Disease Classification using Leung-Malik Filtered Bank Features and Weak Classifier
Shaik Basheera1, M Satya Sai Ram2
1Shaik Basheera, Research Scholar, Department of Electronics and Communication Engineering, Acharya Nagarjuna University Guntur, Andhra Pradesh, India.
2Dr M Satya Sai Ram, Associate Professor, Department of Electronics and Communication Engineering, RVR&JC College of Engineering Guntur, Andhra Pradesh, India.
Manuscript received on 6 August 2019. | Revised Manuscript received on 14 August 2019. | Manuscript published on 30 September 2019. | PP: 1956-1961 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4484098319/19©BEIESP | DOI: 10.35940/ijrte.C4484.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: We propose a frame work to classify Brain MRI images in to Alzheimer’s Disease (AD), Cognitive Normal (CN) and Mild Cognitive impairments (MCI). We use 114No’s of T2 weighted MRI Volumes. We extracted relative texture features from Leung-Malik Filter bank, k means is used to generate Bag of Dictionary (BoD) from LM Filtered images. We performed binary classification and Multi class Classification using different Classifiers, Adaboost Classifier gives better performance both in binary and multi class classifications in comparison with other classifiers. Performance of proposed system is enhanced than compared to the existing techniques. It has Sensitivity for AD-CN 89.8, AD-MCI 78.82, AD-CN-MCI 77.77, Specificity for AD-CN79.22, AD-MCI 80.00, AD-CN-MCI 58.88, and positive prediction value for AD-CN 79.48, AD-MCI 83.75, AD-CN-MCI 68.47 and Accuracy AD-CN 84.24, AD-MCI 79.33, AD-CN-MCI 72.88.
Keywords: Machine Learning, Image Segmentation, Feature Extraction, Support Vector Machines, Gaussian NB, Decision Trees, AdaBoost
Scope of the Article: Classification.