Detection of Brain Tumor using K-Nearest Neighbor (KNN) based Classification Model and Self-Organizing Map (SOM) Algorithm
S. G. Raja1, K. Nirmala2

1S. G. Raja, Research Scholar, Department of Computer Science, Vels University, Chennai, India.
2Dr. K. Nirmala, Research Supervisor, Department of Computer Science, Quaid-e-millath college for women, Chennai, India.
Manuscript received on 02 June 2019 | Revised Manuscript received on 10 June 2019 | Manuscript published on 30 June 2019 | PP: 787-791 | Volume-8 Issue-8, June 2019 | Retrieval Number: F3816048619/19©BEIESP
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Abstract: Knowledge discovery is also known as Data mining in databases, in recent years that technique plays a major role in research area. Data mining in healthcare domain has noteworthy usage in real world. The mining method can enable the healthcare field for the enhancement of institutionalization of its administrations and become quicker with best in class technologies. Innovation utilization isn’t restricted to basic leadership in undertakings, yet spread to different social statuses in all fields. In this paper a novel approach for the detection of brain tumor is proposed. The novel approach uses the classification technique of K-nearest neighbor (KNN) and for ignoring the error of the dataset image SOM (self-organizing map) algorithm has been used. Discrete wavelet transform (DWT) is used for transforming input image data set, in which RGB color of input data image has been converted into gray scale. Then it has been classified using KNN after that the error avoiding algorithm has been carried out. This will help to differentiate tumor cells and the normal cells. The presence of tumor in brain image is detected using parametric analysis by simulation.
Keyword: Data mining, Medical data mining, K-nearest neighbor (KNN), SOM (self-organizing map) algorithm, DWT (discrete wavelets transform).
Scope of the Article: Classification.