2018-2019 Machine Learning Projects


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The Year 2018-2019 IEEE Proects on Machine Learning Projects

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Abstract

Abstract— This Projects proposes segmentation of MRI brain tumor using cellular automata and classification of tumors using Gray level Co-occurrence matrix features and artificial neural network. In this technique, cellular automata (CA) based seeded tumor segmentation method on magnetic resonance (MR) images, which uses volume of interest (VOI) and seed selection. Seed based segmentation is performed in the image for detecting the tumor region and then highlighting the region with help of level set method. The brain images are classified into three stages that are normal, benign and malignant. For this non knowledge based automatic image classification, image texture features and Artificial Neural Network are employed. The conventional method for medical resonance brain images classification and tumors detection is by human inspection.

CLASSIFICATION OF MRI BRAIN TUMOR BASED ON NEURAL NETWORKS Dataset
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2018 Machine Learning Projects in Bangalore


Why Machine Learning
Apply for our Machjine larning Course!2018-2019 OFFERS are available
Machine learning deals with the issue of how to build programs that improve their performance at some task through experience. Machine learning algorithms have proven to be of great practical value in a variety of application domains. Not surprisingly, the field of software engineering turns out to be a fertile ground where many software development and maintenance tasks could be formulated as learning problems and approached in terms of learning algorithms. This paper deals with the subject of applying machine learning methods to so are engineering. In the paper, we first provide the characteristics and applicability of some frequently utilized machine learning algorithms. We then summarize and analyze the existing work and discuss some general issues in this niche area. Finally we offer some guidelines on applying machine learning methods to software engineering tasks.