Enhanced Brain Tumor Detection Using Integrated CNN-ViT Framework: A Novel Approach for High-Precision Medical Imaging Analysis

Abstract

Brain tumors, whether benign or malignant, present significant challenges in medical diagnosis and treatment. Timely and precise detection is critical for effective intervention and patient outcomes. This study introduces a pioneering method for brain tumor detection, employing a fusion of Convolutional Neural Networks (CNN) and Vision Transformer (ViT) architectures. By integrating these models, we exploit their complementary features in image analysis, particularly in medical imaging contexts. Our research assesses the performance of this integrated CNN-ViT framework across various brain tumor imaging modalities and clinical scenarios using extensive experimentation on benchmark datasets. Results validate the robustness and accuracy of our approach, achieving a remarkable precision, recall rates, and overall accuracy of 98%.