An Examination of Brain Tumor Using the ABC and ANN Algorithms

In the last 10 years, computer vision and machine learning have made tremendous advancements and brought about changes in a variety of fields, including the biological field. In the field of biomedicine, it has shown fantastic results, particularly when it comes to the diagnosis of brain cancers via the use of magnetic resonance imaging (MRI) images. The use of deep learning has proven to be extremely effective in the processing of massive volumes of data. In recent years, deep learning has been used in research to identify and classify brain tumors in MRI scans. The purpose of this study is to conduct a comprehensive review of previous research projects that have included deep learning. The purpose of this study is to provide specialists in the field of deep learning with the opportunity to put their knowledge of brain tumor detection and classification to use. Before beginning its investigation, the study examines previous studies that, between the years 2015 and 2023, used deep learning to categorize and identify brain cancers. In this particular circumstance, the result of the research explores both the benefits and the drawbacks associated with the use of deep neural networks. Future researchers will be able to complete a comprehensive analysis of all previously conducted investigations and get an understanding of the efficacy of various deep learning approaches as a consequence of the findings of this study. research thinks that the outcomes of this study will considerably improve the process of locating the tumor. Discovering the precise location of the tumor may be a laborious and difficult process because the size of the tumor might change depending on the MRI imaging. Many algorithms are currently having difficulty locating these difficult assignments. The purpose of our implementation of two algorithms, namely ABC and ANN, is to determine which method provides the better result in comparison to another algorithm. Our suggested model not only allows us determine correctness, but it also reduces the amount of time spent on errors in comparison to existing models.