Robust Brain Tumor Detection and Classification From Multichannel MRI Using Deep Learning

Abstract

Brain tumor detection and classification from multichannel magnetic resonance imaging (MRI) using deep learning techniques for an accurate detection and classification of brain tumors from multichannel MRI are essential for guiding effective treatment strategies and improving patient outcomes. Traditional methods often struggle with handling large volumes of MRI data, leading to limitations in both efficiency and reliability. This study aims to develop a robust approach for brain tumor detection and classification by leveraging computer vision and deep learning techniques, addressing the limitations of conventional methods. The proposed approach utilizes the dual boundary-sensitive transformation (DBST) algorithm for precise tumor edge detection, whereas the scale-invariant feature transform (SIFT) method provides robust and invariant features for classification. Additionally, deep learning models, DarkNet53 and DenseNet201, are employed to enhance classification performance by learning complex patterns from a large dataset of multichannel MRI images. The dataset used in this study is publicly available, ensuring reproducibility and accessibility of the research. The results show a specificity of 98%, indicating the model's strong ability to correctly identify negative cases, and a sensitivity of 99%, demonstrating its effectiveness in identifying positive cases. This performance significantly surpasses traditional methods and is competitive with state-of-the-art (SOTA) techniques in the field. MATLAB is utilized to implement the models, showcasing the potential of deep learning in medical imaging. Future work will explore more advanced deep learning architectures, incorporate additional modalities, and further refine the techniques to improve accuracy and robustness in brain tumor detection and classification.