Abstract
Researchers are increasingly interested in leveraging the Internet of Things in medical and healthcare systems to provide better solutions such as remote health monitoring, personal fitness, and chronic disease management. A brain tumor is a potentially fatal cancer caused by the uncontrollable growth of brain cells that affects human blood cells and nerves. However, accurate classification of brain tumors is difficult due to the vastly different anatomical structures of healthy and tumorous tissues. We propose a framework for brain tumor localization and classification based on CenterNet. The proposed method uses the ResNet34 model with an attention block as a base network, which improves feature representation capacity by focusing on tumor locations and aids in tumor classification, particularly for a small tumor. Our method achieves 98.98% accuracy overall. Both qualitative and quantitative analysis demonstrated the efficacy of our approach for accurate detection and classification of the brain tumor than existing latest approaches.