An Ensemble Deep Learning Approach For Accurate Classification Of Glioma Brain Tumors From MRI Images

Abstract

Brain tumor classification plays a crucial role in early diagnosis and treatment planning of neurological diseases. Among different brain tumors, Gliomas are particularly challenging to diagnose due to their heterogeneous structure and varying appearance in medical images. Magnetic Resonance Imaging (MRI) is widely used for brain tumor detection; however, manual interpretation of MRI scans is time-consuming and highly dependent on expert knowledge. Recent advances in deep learning have significantly improved automated medical image analysis, particularly through Convolutional Neural Networks (CNNs).

In this study, we propose an ensemble deep learning framework for accurate classification of Glioma brain tumors using MRI images. The proposed approach integrates multiple convolutional neural network architectures to enhance feature extraction and improve classification performance. MRI images are first preprocessed to enhance image quality and reduce noise before being fed into the ensemble model. The predictions of individual deep learning models are combined to achieve more robust and reliable classification results.

The proposed framework is evaluated using standard performance metrics including accuracy, precision, recall, and F1-score. Experimental results demonstrate that the ensemble model outperforms individual deep learning models and improves the overall classification performance. The proposed approach provides a reliable and efficient tool for automated Glioma tumor classification and has the potential to assist clinicians in early diagnosis and treatment planning.