Glioma Tumor's Detection and Classification Using Joint YOLOv7 and Active Contour Model

Abstract

In this paper, a multi-stage deep learning model is proposed for brain glioma tumor detection and segmentation from MRI scans. The model consists of two stages: object detection using YOLOv7 with EfficientNet-B0 backbone, and active contour snake model for boundary refinement and segmentation. The proposed method also includes a customized CNN with feature selection and GRU layer for accurate class label prediction. The proposed model has been trained on the BraTS 2020 dataset and has achieved state-of-the-art performance in terms of accuracy and effectiveness. This proposed method can potentially assist radiologists and clinicians in detecting and segmenting brain tumors in medical images, leading to better diagnosis and treatment planning for patients.