RRFNet: A free-anchor brain tumor detection and classification network based on reparameterization technology
Wei Liu, Xingxin Guo
TL;DR
This paper introduces RRFNet, a deep learning model for brain tumor detection that improves accuracy and speed compared to existing methods.
Contribution
The novel RRFNet uses reparameterization and a new feature map module for efficient and accurate brain tumor detection.
Findings
RRFNet achieves a mAP of 79.2%, outperforming YOLOv8 in brain tumor detection.
Using RepConv and RepC3 reduces model parameters while maintaining high accuracy.
Increasing the number of brain tumor samples improves detection performance.
Abstract
Advancements in medical imaging technology have facilitated the acquisition of high-quality brain images through computed tomography (CT) or magnetic resonance imaging (MRI), enabling professional brain specialists to diagnose brain tumors more effectively. However, manual diagnosis is time-consuming, which has led to the growing importance of automatic detection and classification through brain imaging. Conventional object detection models for brain tumor detection face limitations in brain tumor detection owing to the significant differences between medical images and natural scene images, as well as challenges such as complex backgrounds, noise interference, and blurred boundaries between cancerous and normal tissues. This study investigates the application of deep learning to brain tumor detection, analyzing the effect of three factors, the number of model parameters, input data…
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Taxonomy
TopicsBrain Tumor Detection and Classification · Advanced Neural Network Applications · COVID-19 diagnosis using AI