Application of YoloV5 for Brain Tumor Detection from MRI

The research paper introduces an innovative method for detecting brain tumors using a deep-learning framework called YoloV5. This framework is widely recognized for its ability to identify objects in time. To conduct this study, we utilized a dataset called “br35h,” which contains a collection of brain scans carefully annotated with tumor information. These scans were meticulously prepared using the Maksense.ai platform. The main objective of our research is to evaluate the effectiveness of the YoloV5 algorithm in identifying tumor regions within brain images. We emphasize the detection of tumors and highlight the advantages of cutting-edge deep-learning techniques. In order to establish a foundation for our proposed approach, we thoroughly examine existing methods for detecting brain tumors and compare them with YoloV5. Our methodology includes explaining the dataset, outlining preprocessing procedures, and discussing adaptations made to the YoloV5 algorithm to improve its performance in detecting brain tumors. This study delves into training and annotating using the Maksense.ai platform. We discuss optimization strategies and crucial parameters that were employed. Our focus lies heavily on evaluating performance, where we examine metrics like mean Average Precision (mAP) loss, precision, recall, and F1 score to comprehensively analyze how effective the model is. To present our results visually, we employ charts, graphs, and visualizations demonstrating the algorithm's ability to identify tumor areas accurately. Furthermore, we compare our approach with established methodologies to highlight its advantages.