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The accurate and precise identification of brain tumors is essential for appropriate diagnostic and treatment strategy formulation. The EfficientNet-B0 CNN architecture uses deep learning to improve brain tumor diagnosis by automating feature extraction, hierarchical pattern identification, and learning from massive medical imaging datasets. This chapter investigates the utilization of EfficientNet-B0, a cutting-edge convolutional neural network (CNN) architecture, for the detection of brain tumors in medical imaging datasets. In contrast to typical CNNs, EfficientNet-B0’s compound scaling technique optimizes depth, width, and resolution simultaneously, improving accuracy and lowering computing costs. EfficientNet-B0 outperformed CNNs and support vector machine (SVM) classifiers in tumor detection and type with a 99.96% training accuracy, a 97.62% validation accuracy, and a 98.47% testing accuracy. The suggested method also highlights preprocessing techniques, such as image augmentation and normalization (i.e., using min-max scaling or Z-score normalization), to improve model resilience. To help radiologists, EfficientNet-B0 could be integrated into clinical operations; however, regulatory approvals, dataset bias, model interpretability, and interaction with existing medical workflows are obstacles. EfficientNet-B0's accuracy, precision, recall, F1-score, and inference speed show that it is reliable (high accuracy and recall), efficient (low computational cost and inference time), and scalable.

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Deep Learning Techniques to Detect Brain Tumors Using the EfficientNet-B0 CNN Architecture

  • 章节

  • 发表日期：

    2025年

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  • Pradeep K. Parhi,
  • Sthitapragyan Mohanty,
  • Prashanta K. Patra,
  • Asit Mohanty

摘要

The accurate and precise identification of brain tumors is essential for appropriate diagnostic and treatment strategy formulation. The EfficientNet-B0 CNN architecture uses deep learning to improve brain tumor diagnosis by automating feature extraction, hierarchical pattern identification, and learning from massive medical imaging datasets. This chapter investigates the utilization of EfficientNet-B0, a cutting-edge convolutional neural network (CNN) architecture, for the detection of brain tumors in medical imaging datasets. In contrast to typical CNNs, EfficientNet-B0’s compound scaling technique optimizes depth, width, and resolution simultaneously, improving accuracy and lowering computing costs. EfficientNet-B0 outperformed CNNs and support vector machine (SVM) classifiers in tumor detection and type with a 99.96% training accuracy, a 97.62% validation accuracy, and a 98.47% testing accuracy. The suggested method also highlights preprocessing techniques, such as image augmentation and normalization (i.e., using min-max scaling or Z-score normalization), to improve model resilience. To help radiologists, EfficientNet-B0 could be integrated into clinical operations; however, regulatory approvals, dataset bias, model interpretability, and interaction with existing medical workflows are obstacles. EfficientNet-B0's accuracy, precision, recall, F1-score, and inference speed show that it is reliable (high accuracy and recall), efficient (low computational cost and inference time), and scalable.