A Comparative Study for Evaluating the Performance of Various Classification Techniques on Brain Tumour Images

Image processing methods have become consistently used approaches in a wide variety of applications with modern technology devices. Image classification and segmentation is a common topic in computer vision as well as a backbone and focal point in image processing approaches. Biometric and biomedical image processing are two of the most common examples of image processing applications that we have seen over the past two decades. in medicine, there is a great need for modern imaging technology algorithms that can be used to detect the internal organs of the human body in the process of disease diagnosis. To this end, researchers are working every day to develop new methods or improve existing methods that will help in efforts to improve human life and health. in this study, a comparative study was conducted to evaluate the performance of different types of machine learning classification algorithms on brain tumor images.  According to the result obtained, the performance of the models differs in the accuracy of their classification capabilities. Different performance measurements are used during the evaluation and interpretation of the results and the results obtained are presented in the comparison table in the results section. Six classification models are used, which are mainly used in the field of Machine Learning (ML) and Artificial Intelligence (AI). Future studies may increase the comparison techniques by adding other classification and clustering techniques to study the effectiveness of each technique in the classification operation of medical images.