Abstract
Abstract: Brain tumors are abnormal enlargements of nerves that disrupt normal brain function and can cause death. Locating tumor-affected brain cells can be time-consuming. Early detection and appropriate treatment are critical for saving lives. However, conventional methods for identifying brain tumors through image processing have limitations in both accuracy and processing speed. In this study, we propose using the EfficientNet architecture, a cutting-edge convolutional neural network (CNN), to improve the accuracy and efficiency of brain tumor detection. We present a comprehensive approach that includes preprocessing steps to prepare the input data and data augmentation techniques to enhance the dataset, thus improving the CNN model's generalization and robustness.
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