Brain tumor detection and classification with DGMM: RBCNN technique

U Sandhya; K Naresh Kumar; A P Saranya; N Jayapal; S. Kumarganesh

doi:10.53730/ijhs.v6nS1.6911

Authors

Sandhya. U Assistant professor, Department of ECE, GRT Institute of Engineering and Technology, Tiruttani
K Naresh Kumar Assistant professor/ECE, GRT Institute of Engineering and Technology, Tiruttani
Saranya A P Assistant professor, ECE, GRT Institute of Engineering and Technology, Tiruttani
N. Jayapal Assistant Professor, Department of ECE, Kongunadu College of Engineering and Technology, Trichy – 621215, Tamilnadu
S. Kumarganesh Professor, Department of ECE, Knowledge Institute of Technology, Salem-637504, Tamilnadu

Keywords:

Corner Net Model-Faster R-CNN, brain tumor, MRI, Feature Extraction, Segmentation, Gray Level Co-occurrence Matrix

Abstract

Glioblastoma Multiforme, which accounts for 80% of malignant primary brain tumors in adults, is divided into two types: High Grade Glioma (HGG) and Low Grade Glioma (LGG). LGG tumors are less aggressive than HGG tumors, growing at a slower rate and responding to treatment. Because tumor biopsy is difficult for people with brain tumors, non-invasive imaging methods such as Magnetic Resonance Imaging (MRI) have been widely used to diagnose brain cancers. We examine Deep Convolutional Neural Networks (ConvNets) for brain tumor classification utilising multisequence MR data in this paper. Early detection of the tumor is possible with artificial intelligence-based solutions. This manner, a tumor might be detected early and a condition that could risk human life could be resolved. The architecture was used to detect probable brain cancers early, which constitute a serious threat to human life.

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