Brain Tumor Detection Using YoloV5 and Faster RCNN
Keywords:Brain tumor detection, MRI images, thresholding techniques, Otsu thresholding, YOLOv5, Faster R-CNN, deep learning, comparative analysis, medical imaging
Accurate detection of brain tumors in medical imaging plays a crucial role in early diagnosis and treatment planning. In this paper, we propose two distinct methodologies for brain tumor detection from MRI scans: the traditional thresholding techniques and the advanced YOLOv5 and Faster R-CNN object detection algorithms. In the first approach, we employ thresholding methods, including the Otsu thresholding technique, to segment MRI images and identify potential tumor regions based on pixel intensity variations. This straightforward yet practical approach aims to isolate potential abnormalities within the brain tissue swiftly. In the second approach, we harness the power of deep learning by implementing the YOLOv5 and Faster R-CNN algorithms. These state-of-the-art object detection techniques are trained on a dataset of MRI images with annotated tumor regions. The models' ability to learn intricate patterns and features enables them to locate brain tumors amidst complex anatomical structures accurately. Our experiments comprehensively evaluate both approaches using diverse metrics such as precision, recall, F1-score, and Intersection over Union (IoU). Through these evaluations, we elucidate the strengths and weaknesses of each method concerning accuracy, speed, and adaptability to varying image qualities and tumor types. The outcomes of our study reveal intriguing insights. The thresholding techniques demonstrate efficiency and simplicity, making them suitable for rapid initial assessments. However, deep learning models showcase superior accuracy and robustness, particularly when faced with intricate tumor patterns and varying imaging conditions.
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