Development of KNN-EIM based hybrid model for brain MRI density range determination

Abstract

Data mining techniques are used in the medical data analysis to predict the hidden knowledge from the existing data. This work attempted to apply the data mining techniques to analysis the medical image focus to brain tumor evaluation. The affected tumor region range values. The work focused to evaluate the performance of clustering algorithms for medical image analysis via predicting the tumor affected area. It is achieved through analysing Brain MRI to identify the high-density neuron using the integrated approach of K-Means and Equal Interval clustering. The tumour affected area and its range values are identified using a clustering algorithm such as Equal Interval Method and K-Means approach. To enhance the accuracy of the affected region, the common affected high-density pixels are identified using a hybrid model through the Integration of K-Means and Equal Interval Method. It is evaluated according to time complexity and accuracy.