MRI Brain Tumour Detection & Image Segmentation by Hybrid Hierarchical K-means clustering with FCM based Machine Learning Model

Abstract

Introduction: A brain tumor tends to be a significant public health problem that affects many forms of living. To obtain an objective diagnostic to evaluate the best suitable medical plan, which derived data is used to classify the critical features within brain tumors i.e. position, size, and specific type.

Background: This research article developed a hybrid model called Hierarchical K-means clustering with Fuzzy c and Super Rule tree (HKMFSRT-Model) to introduce an asynchronous image recognition methodology for detection and identification of brain tumors throughout MRI images with their very beginning phases.

Methods: The suggested HKMFSRT-Model seems to be a hybrid of clustering k-means, Super-Rule-Tree, image acquisition utilizing patch-based system development, as well as to object counting approach. Several techniques need to have a predetermined fixed frame rate in advance to identify the patterns.

Results & Discussion: The proposed methodology uses a super-rule construct to formulate a plus-Rule-Tree to face the issue of misplaced patterns. Proposed method has accuracy result 88.9 %, and existing k-Means clustering method showing accuracy 85.4 %.

Conclusions: Beyond medical application, the procedure can also be incorporated into automatic treatment technologies and complex surgical applications.