Brain Anomaly Prediction with the Intervention of Fuzzy Based Clustering and Optimization Techniques for Augmenting Clinical Diagnosis
Kottaimalai Ramaraj, Vishnuvarthanan Govindaraj, Yudong Zhang, Pallikonda Rajasekaran Murugan, Arunprasath Thiyagarajan
Abstract
The dimension of diagnosing and treating a patient has been dynamic, and the agony experienced by the patients has taken innumerable forms and measures in recent years. Morbidity rate has been drastically reduced with the intervention of technology and advancements in drugs and medicines, and there are some unsettled scores, which are of greater concern to doctors and technologists pioneering in the healthcare industry as well. Most of the disease prediction models and techniques have been tweaked to the greatest extent, and they have evolved in due course to be the cornerstone in patient diagnosis. One specific matter of concern that needs to be focused on for the betterment of the patients is the prediction of odious regions that scathe the very existence and functionality of the human brain. Such regions are embedded deep within the brain, posing a severe challenge to the radiologists in the process of diagnosis, and causing greater harm to the patients’ health as well. The combo of Fuzzy based clustering and optimization technique methodology recommended through this paper addresses the issue mentioned, and it really supports in augmenting the patient diagnosis processes. The competency of the methodology insinuated through this paper has been validated and justified using some of the benchmark metrics such as MSE, PSNR, TC, DOI, computational time, and the values for the same have been found to be as 0.0043, 71.9869 dB, 30.487%, 46.632%, and 19.26 seconds, respectively. Some other positive traits of the algorithm developed are discussed in-depth in this paper, and they will be of matter of importance/significance to doctors as well as technologists.