DEEP Q-NAS: A new algorithm based on neural architecture search and reinforcement learning for brain tumor identification from MRI

Abstract

A significant obstacle in brain tumor treatment planning is determining the tumor’s actual size. Magnetic resonance imaging (MRI) is one of the first-line brain tumor diagnosis. It takes a lot of effort and mostly depends on the operator’s experience to manually separate the size of a brain tumor from 3D MRI volumes. Machine learning has been vastly enhanced by deep learning and computer-aided tumor detection methods. This study proposes to investigate the architecture of object detectors, specifically focusing on search efficiency. In order to provide more specificity, our goal is to effectively explore the Feature Pyramid Network (FPN) and prediction head of a straightforward anchor-free object detector called DEEP Q-NAS. The study utilized the BraTS 2021 dataset which includes multi-parametric magnetic resonance imaging (mpMRI) scans. The architecture we found outperforms the latest object detection models (like Fast R-CNN, YOLOv7, and YOLOv8) by 2.2 to 7 points with average precision (AP) on the MS COCO 2017 dataset. It has a similar level of complexity and less memory usage, which shows how effective our proposed NAS is for object detection. The DEEP Q-NAS with ResNeXt-152 model demonstrates the highest level of detection accuracy, achieving a rate of 99%.