Semantic-Aware Hybrid Deep Learning Model for Brain Tumor Detection and Classification Using Adaptive Feature Extraction and Mask-RCNN

A brain tumor is one of the most prevalent causes of cancer death. The best strategy is the timely treatment of brain tumors in their early detection. Magnetic Resonance Imaging (MRI) is a standard non-invasive method to detect brain tumors. For early detection and better patient survival through MRI scans, the diagnosis needs a high level of knowledge in the radiological and neurological domains to identify the cancers. Researchers have suggested various brain cancer detection techniques. However, most existing automatic cancer detection approaches suffer from poor accuracy and low detection rates. This paper proposes a hybrid deep learning (DL) using deep feature extraction and adaptive Mask Region-based Convolutional Neural Networks (Mask-RCNNs) model for brain tumor detection and classification method to overcome these issues. The experimental findings on the benchmark dataset demonstrate that the planned model is highly effective, with 99.64% accuracy, 95.93% precision, 95.39% recall, and 95.67% F1-score.