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Home / programming language / Online Dotnet Projects / Fusion of Contourlet Transform and Zernike Moments Feature Extraction using DNN and ELM Classifiers for MRI Brain Tumor Images
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Fusion of Contourlet Transform and Zernike Moments Feature Extraction using DNN and ELM Classifiers for MRI Brain Tumor Images

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SKU: PROJ7687 Categories: 2017 Projects, Datamining Projects, Final Year Projects, Online Dotnet Projects, programming language Tags: Android Project, Asp.Net Project, C# Project, Computer Engineering Final Year Projects, Computer Science Final Year Projects, Electronics Projects Engineering Students Final Year, Engineering Student Project Ideas, Final Semester Projects, Final Year Project Center, Final Year Projects, ieee ECE Projects, ieee EEE Projects, ieee Final Year Projects, IEEE Projects, ieee Projects Networking, Image Processing Projects, J2EE Project, Java Project, Java Project Code, Matlab Project, PHP Project, Students Projects
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Fusion of Contourlet Transform and Zernike Moments Feature Extraction using DNN and ELM Classifiers for MRI Brain Tumor Images

Abstract— Image retrieval is that the most vital application that has been used extensively in image processing. Content Based Image Retrieval (CBIR) is employed to search and retrieve the expected image from the database. Magnetic resonance imaging (MRI) technique plays a crucial role in diagnosing many diseases in human brain. A fusion technique for T1 and T2 weighted MRI scans. Our proposed technique has three parts. First, texture and shape features are extracted from a brain tumor images. Next, the fusion techniques like genetic algorithm (GA) and particle swarm optimization (PSO) are used to combine the texture and shape features. Finally, the popular supervised learning machine techniques like Deep neural network (DNN) and Extreme learning machine (ELM) are used to classify the brain tumor based on the selected features. The experiment is done on 1000 brain tumor images. Six measures, namely sensitivity, specificity, accuracy, error rate, Jaccard coefficient and f-measure are used to evaluate the performance of the method. < final year projects >

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