ICTACT Journal on Image and Video Processing

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Classification of Brain Tumor using Bees Swarm Optimisation


Affiliations
1 Department of Computer Science and Engineering, Gnanamani College of Technology, India
2 Department of Computer Science and Engineering, Sri Satya Sai University of Technology and Medical Sciences, India
     

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Nowadays, processing the medical image is a most significant diagnostic process. Usually RMI is used to detect the presence of and type of tumor. The following process is very complicated in the brain tumor classification. The treatment of medical images, such as image segmentation, image extraction, and image classification, takes various steps. Various types of properties such as intensity, forms and texture-based features are extracted from a segmented MRI image. The feature selection approach is employed to select a small subset of MRI image features that minimize redundancy and maximize target-related pertinence. This article uses the Bees Swarm Optimization (BSO) for the selection and the Neural Network Classifier to classify the type of tumor in present brain MRI images, and then takes online MRI images which contain brain tumor, then a machine-learning model.

Keywords

Neural Network, ACO, Feature Extraction, Classification, MRI.
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  • Classification of Brain Tumor using Bees Swarm Optimisation

Abstract Views: 244  |  PDF Views: 1

Authors

M. Ramkumar
Department of Computer Science and Engineering, Gnanamani College of Technology, India
M. Babu
Department of Computer Science and Engineering, Sri Satya Sai University of Technology and Medical Sciences, India
R. Lakshminarayanan
Department of Computer Science and Engineering, Gnanamani College of Technology, India

Abstract


Nowadays, processing the medical image is a most significant diagnostic process. Usually RMI is used to detect the presence of and type of tumor. The following process is very complicated in the brain tumor classification. The treatment of medical images, such as image segmentation, image extraction, and image classification, takes various steps. Various types of properties such as intensity, forms and texture-based features are extracted from a segmented MRI image. The feature selection approach is employed to select a small subset of MRI image features that minimize redundancy and maximize target-related pertinence. This article uses the Bees Swarm Optimization (BSO) for the selection and the Neural Network Classifier to classify the type of tumor in present brain MRI images, and then takes online MRI images which contain brain tumor, then a machine-learning model.

Keywords


Neural Network, ACO, Feature Extraction, Classification, MRI.

References