Brain Tumor Detection with YOLOv8
The findings highlight the importance of optimization strategies in deep learning-based analysis of medical images and demonstrate the capability of YOLO v8 as a robust tool for brain tumor detection.
Abstract
Brain tumors are cancer causing cells which when developed inside a person’s brain can cause them life threatening challenges. The major objective of this study is to detect brain tumors in the early stages of the disease by using a deep learning algorithm, YOLO v8, in the hopes of increasing the survival rates of the patients. The dataset is acquired from kaggle which contains annotated MRI images of brain tumors that belong to either meningioma, glioma, no tumor and pituitary class. To optimize the training process, diverse sizes and optimizers are used. Through this extensive experimentation the findings are recorded. The findings highlight the importance of optimization strategies in deep learning-based analysis of medical images and demonstrate the capability of YOLO v8 as a robust tool for brain tumor detection.