Brain Tumor Classification with Optimized EfficientNet Architecture
DOI:
https://doi.org/10.52783/jns.v14.3990Keywords:
Brain Tumor Classification, EfficientNet, Deep Learning, Convolutional Neural Networks (CNN), MRI Image Analysis, Medical Image ProcessingAbstract
Classification of brain tumors is essential for early identification and efficient patient treatment planning. The necessity for automated, precise, and effective categorization systems derives from the time-consuming and human error-prone nature of traditional diagnostic techniques. In this work, we suggest a deep learning-based method for classifying brain cancers from MRI images using an optimized EfficientNet architecture. To improve its performance on brain tumor datasets, EfficientNet - which is renowned for striking a compromise between accuracy and computing efficiency - is adjusted using transfer learning and hyperparameter optimization. A publicly accessible dataset of MRI images classified as gliomas, meningiomas, pituitary tumors, and no tumor is used to train and assess our algorithm. To enhance generalization and lessen overfitting, the optimization incorporates dropout regularization, learning rate scheduling, and data augmentation. According to experimental data, the optimized EfficientNet model achieves over 95% classification accuracy and performs better than traditional CNN architectures in terms of accuracy, precision, recall, and F1-score. The suggested approach provides a reliable and expandable way to diagnose brain tumors in real time, which could help radiologists make clinical decisions. To improve diagnostic performance even further, future research will investigate the integration of multimodal data and attention mechanisms.
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