QUANTUM BASED BINARY BAT DEEP LEARNING METHOD FOR BRAIN TUMOR CLASSIFICATION IN MRI IMAGES

Abstract

In order to accurately diagnose, treat, and manage a variety of diseases, it is essential to havemedical images that are stored, analyzed, and transmitted in a manner that is both efficient anddependable. Numerous studies in this sector have concentrated on the utilization of quantumand quantum-inspired algorithms to boost the performance of traditional medical imageprocessing procedures. The model that has been proposed is an mix of quantum-basedalgorithms and algorithms inspired by nature, and it incorporates the more hopeful aspects ofboth types of algorithms. By utilizing the quantum-based binary bat algorithm, also known as q-BBA, the proposed model has been successful in reducing dimensionality, which refers toaspects that are not necessary. QBBA achieved superior results than its conventional algorithms.This was discovered after comparing the performance of QBBA with that of its conventionalmethods. The QBBA algorithm emerges as a notable algorithm due to its enhanced noiseimmunity. The proposed Quantum-based Binary Bat method has the potential to be applied inthe detection of brain tumors