YOLOv5x-based Brain Tumor Detection for Healthcare Applications

Abstract

Brain tumors arise from the emergence of abnormal cells in brain tissue and are considered one of the most perilous conditions affecting individuals of all ages, including both children and adults. The disease advances swiftly, and the likelihood of survival diminishes significantly without prompt and adequate treatment. Hence, accurate diagnosis and meticulous treatment planning play a pivotal role in improving the patient's life expectancy. Neurologists and radiologists play a crucial role in the early detection of brain tumors. However, manually identifying and segmenting brain tumors from Magnetic Resonance Imaging (MRI) data poses significant challenges and is susceptible to inaccuracies. The need for an automated brain tumor detection method becomes imperative to achieve early detection of brain tumors.Objective: The objective of the paper is to measure the capability of You Only Look Once version 5 (YOLOv5x) in brain tumor detection in the early stage so that patients can be treated accordingly.Methods: YOLOv5x is examined; Brats (Brain Tumor Segmentation) image and roboflow dataset has been used. Model performance is evaluated using precision, recall rate, F1 score, and Mean Average Precision (mAP).Results: YOLOv5x exhibited precision (98.7), recall (95.6), F1-score (93), mAP at a learning rate of 0.5 (98.4), and the total time taken for implementation of work is 193.20 minutes.Conclusion: YOLOv5x showed improved performance for the detection of brain tumors on dynamic contrast-enhanced MRI when compared with state of art existing work. It is also the fastest and accurate method indicating a greater potential for clinical application.