Deep Brain Net: An Optimized Deep Learning Model for Brain tumor Detection in MRI Images Using EfficientNetB0 and ResNet50 with Transfer Learning

• URL: http://arxiv.org/abs/2507.07011v1
• Date: Wed, 09 Jul 2025 16:42:26 GMT
• Title: Deep Brain Net: An Optimized Deep Learning Model for Brain tumor Detection in MRI Images Using EfficientNetB0 and ResNet50 with Transfer Learning
• Authors: Daniel Onah, Ravish Desai,
• Abstract summary: We propose Deep Brain Net, a novel deep learning system designed to optimize performance in the detection of brain tumors.<n>The model integrates the strengths of two advanced neural network architectures which are EfficientNetB0 and ResNet50.<n>Extensive experiments performed on publicly available MRI datasets demonstrate that Deep Brain Net consistently outperforms existing state of the art methods in terms of classification accuracy, precision, recall, and computational efficiency.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, deep learning has shown great promise in the automated detection and classification of brain tumors from MRI images. However, achieving high accuracy and computational efficiency remains a challenge. In this research, we propose Deep Brain Net, a novel deep learning system designed to optimize performance in the detection of brain tumors. The model integrates the strengths of two advanced neural network architectures which are EfficientNetB0 and ResNet50, combined with transfer learning to improve generalization and reduce training time. The EfficientNetB0 architecture enhances model efficiency by utilizing mobile inverted bottleneck blocks, which incorporate depth wise separable convolutions. This design significantly reduces the number of parameters and computational cost while preserving the ability of models to learn complex feature representations. The ResNet50 architecture, pre trained on large scale datasets like ImageNet, is fine tuned for brain tumor classification. Its use of residual connections allows for training deeper networks by mitigating the vanishing gradient problem and avoiding performance degradation. The integration of these components ensures that the proposed system is both computationally efficient and highly accurate. Extensive experiments performed on publicly available MRI datasets demonstrate that Deep Brain Net consistently outperforms existing state of the art methods in terms of classification accuracy, precision, recall, and computational efficiency. The result is an accuracy of 88 percent, a weighted F1 score of 88.75 percent, and a macro AUC ROC score of 98.17 percent which demonstrates the robustness and clinical potential of Deep Brain Net in assisting radiologists with brain tumor diagnosis.

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