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<p>Brain tumors are among the most fatal diseases, Often leading to a reduction in life expectancy. Early and accurate diagnosis is essential to guide effective treatment and enhance survival rates. Advances in artificial intelligence, particularly deep learning with Convolutional Neural Networks (CNNs), have revolutionized medical imaging analysis by enabling automated and precise diagnostic tools. This study investigates the effectiveness of deep transfer learning for brain tumor classification using a publicly available 7023 image Brain Tumor MRI Dataset (Figshare, SARTAJ, Br35H), and split into training, validation, and test sets, categorizing four classes : glioma, meningioma, pituitary tumor, and no tumor. The proposed method employs a fine-tuned ResNet-34 model, enhanced with custom classification head, data augmentation techniques, and the Ranger optimizer (combining RAdam and Lookahead for stable convergence). The model achieved an accuracy of 99.66% , surpassing current state-of-the-art approaches.</p>

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Fine-tuned ResNet34 for efficient brain tumor classification

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月22日

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  • Anas Shahin

摘要

Brain tumors are among the most fatal diseases, Often leading to a reduction in life expectancy. Early and accurate diagnosis is essential to guide effective treatment and enhance survival rates. Advances in artificial intelligence, particularly deep learning with Convolutional Neural Networks (CNNs), have revolutionized medical imaging analysis by enabling automated and precise diagnostic tools. This study investigates the effectiveness of deep transfer learning for brain tumor classification using a publicly available 7023 image Brain Tumor MRI Dataset (Figshare, SARTAJ, Br35H), and split into training, validation, and test sets, categorizing four classes : glioma, meningioma, pituitary tumor, and no tumor. The proposed method employs a fine-tuned ResNet-34 model, enhanced with custom classification head, data augmentation techniques, and the Ranger optimizer (combining RAdam and Lookahead for stable convergence). The model achieved an accuracy of 99.66% , surpassing current state-of-the-art approaches.