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@article{206086,
        author = {Abhishek Muppayyanmath and Aditya Nandan and Raheem R J and Dr. Nagamani.S},
        title = {Ensemble-Based Brain Tumor Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {369-377},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206086},
        abstract = {Brain tumors are a major health concern that must be identified accurately and promptly in order to be effectively treated. Because they need a lot of labeled data and might miss intricate patterns in medical images traditional techniques and basic deep learning models have trouble correctly identifying and categorizing various kinds of brain tumors. This study offers sophisticated ensemble deep learning frameworks that enhance brain tumor classification by combining several intelligent systems. The Deep Multiple Fusion Network (DMFN) an intelligent fusion system that combines predictions from multiple neural networks to classify four types of brain tumors—glioma meningioma pituitary and normal cases—with an accuracy of 98.36% is one method that uses a framework that includes data generation techniques to create more training examples feature extraction models. Another complementary method achieves 99.1% accuracy 98.8% precision 98.9% recall and 99.0% F1-measure by processing MRI images using a hybrid model that combines Convolutional Neural Networks with Long Short-Term Memory networks. Ultimately, these ensemble-based solutions have the potential to genuinely improve patient survival rates and real-world clinical outcomes — not just on paper, but in the hands of the physicians and radiologists who rely on these tools every day to make faster, more confident decisions when diagnosing brain tumors.},
        keywords = {Brain Tumor Classification, Ensemble Learning, ResNet18.},
        month = {July},
        }