Enhancing Brain Tumor Detection with ResNet: A Deep Learning Approach

If they are not detected at an early stage, brain tumors are a common and severe kind of malignant illness that may significantly reduce a person’s life expectancy if they are not treated. They comprise a number of different categories, and the treatment approach is determined not only by the kind of tumor but also by its grade and location. If they are not found in time, brain tumors may be lethal. Images obtained using a process known as magnetic resonance imaging (MRI), which requires specialized expertise and is both time-consuming and expensive, are often relied on in order to make a diagnosis of brain tumors. The objective of this study is to enhance the precision and effectiveness of MRI scanners when it comes to classifying and categorizing brain tumors. To achieve this, the ResNet, which pre-trained model is utilized extensively. ResNet was found to be the most trustworthy architectural framework for the detection and classification of medical images after being subjected to an exhaustive analysis of previous research. This analysis revealed that ResNet had a remarkable consecutive accuracy rate of 99%. These improved rates of accuracy play a critical part in the early diagnosis of tumors, which helps prevent the development of physical consequences such as paralysis and other forms of disability.