Smart Identification of High-Density Tumor in Brain using Fish School-based Algorithm and Fuzzy Clustering Technique

Abstract

There are issues on the prediction of the medical image analyzation which leads to inaccuracies and errors made by operator, instrument/devices and environments. These troubles can be made unanimous with the Fish School Optimization (FSO) technique and Interval Type II Fuzzy Logic System (IT2FLS) which solves various topographical locations in brain subjects of MRI modality. This method can be incorporated in clinical practice for better work of both doctors and patients. The above method provides better values than the other techniques for the segmentation of many sequences of various axes coordination. Nearly one quarter of death is reported due to cancer. The morality rate of the people dying due to brain tumour has been increasing every year. Nearly 23.4% of the patients are mistreated due to the error caused by the doctors at the worst-case situation. Therefore, this technique is widely used on processing the Magnetic Resonance Imaging for diagnosing the tumours and other relevant ailments. Various types of imaging techniques such CT scan, X-rays are used for visualizing the anatomical structure of the human brain. But MRI uses low ionizing radiation when compared to X-Rays and CT. And so the differentiation of grey and white matters can be clearly imaged by using MRI scanners. The proposed method will automatically provide accurate results.