A Multi-Head Self-Attention Mechanism for Improved Brain Tumor Classification using Deep Learning Approaches

One of the most common life-threatening diseases, the brain tumor is a condition characterized by the rapid proliferation of abnormal cells that leads to the destruction of healthy brain cells. Its aggressive nature can result in a patient succumbing to the disease before an accurate diagnosis is achieved. Timely detection is crucial to effective treatment and patient survival. Similarly, early detection plays a pivotal role in the case of brain tumors, where swift identification is vital to providing optimal care and increasing the chances of patient recovery. Streamlining the complex process of brain tumor detection is a significant undertaking that aims to simplify and expedite the procedure, ultimately contributing to saving valuable time and enhancing patient outcomes. The proposed model, a modified VGG-16, facilitates faster and more accurate identification of abnormal brain cells, leading to early detection of brain tumors. A novel multihead self-attention mechanism is used in the modified VGG-16 architecture to improve tumor detection performance. The proposed model performs better than other state-of-the-art models, such as normal VGG-16, ResNet-50, and EfficientNet.