Browsing by Author "BERRA Ayyoub"

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    Enhancing Brain Tumor Classification Accuracy through Multi-Modal MRI Analysis and Advanced Ensemble Deep Learning
    (UNIVERSITY OF M’SILA, 2025-06-30) BERRA Ayyoub; ENCA/ ATTALLAH Bilal
    In an attempt to apply state-of-the-art deep learning techniques for brain tumor classification, which is a medical imaging domain, accurate multi-class diagnosis can dramatically affect treatment choices and patient outcomes, two modern CNN architectures EfficientNet-B3 and ConvNeXt-Tiny were evaluated for the classification of 44 different brain tumor types through Magnetic Resonance Imaging (MRI) data. The dataset, sourced from Kaggle. To redress class imbalance and enhance the generalization aspect, several data augmentation methods . Transfer learning was implemented by employing pretrained weights, and both models were fine-tuned with customized classifier heads that have Batch Normalization, Dropout and Dense layers for training. The same hyperparameters were used to train all models, and the evaluation was done with Accuracy, F1-Score, Recall, and Precision. The results demonstrate that EfficientNet-B3 reached an accuracy of 96.85%, F1-Score 96.74%, Recall 96.85%, and Precision 96.94%, while ConvNeXt-Tiny got accuracy of 95.88% ,F1-Score 95.86%, Recall 95.88%, and Precision 96.16%. A weighted ensemble approach was also used to combine the two models. This ensemble model attained the highest classification accuracy of 97.07%, which shows that weighted averaging is a good way to improve performance. Such outcomes demonstrate the strong points of present deep learning architectures and ensemble methods in dealing with fine-grained multi-class brain tumor classification. The findings confirm that hybrid approaches have the capability to surpass accuracy in addressing difficult diagnostic tasks from medical imaging data.