A Review of Optimization Techniques for Classification of Computed Tomography Images

In relation to image processing, this work presents an examination of several optimization techniques. The purpose of the study is to provide a thorough review of the optimization techniques as well as information on the applicability of various optimization methods. In general, traditional approaches fall short when dealing with such complex issues, especially when nonlinear objective functions are involved. Many local optima optimization issues are difficult to solve, and certain optimization strategies commonly fall short. Extending more potent optimization approaches is necessary to overcome these issues. Differential and non-differential optimization problems may be solved using contemporary approaches for linear and nonlinear optimization. This evaluation of the literature gathers several works on optimization and comes to a conclusion about the most precise and acceptable strategy for optimization. Creating an imaging system to assess the issue is the most effective approach to do this, and advise a course of therapy for correction. Medical image analysis's primary goal will be to provide help for medical professionals in particular clinical applications that call for the visual evaluation of medical pictures in order to develop analysis’ consistency and impartiality. A variety of stages are involved in the medical picture analysis. Obtaining medical photos from different medical datacenters, removing essential characteristics, and feature dimension medical picture reduction and categorization using the best characteristics. The goal of this review study was to add up all currently used feature dimension reduction methods that rely on optimization algorithms, and suggested a fresh approach to widen the solution space.