Background/Objectives: Brain tumor classification is a crucial task in medical diagnostics, as early and accurate detection can significantly improve patient outcomes. This study investigates the effectiveness of pre-trained deep learning models in classifying brain MRI images into four categories: Glioma, Meningioma, Pituitary, and No Tumor, aiming to enhance the diagnostic process through automation. Methods: A publicly available Brain Tumor MRI dataset containing 7023 images was used in this research. The study employs state-of-the-art pre-trained models, including Xception, MobileNetV2, InceptionV3, ResNet50, VGG16, and DenseNet121, which are fine-tuned using transfer learning, in combination with advanced preprocessing and data augmentation techniques. Transfer learning was applied to fine-tune the models and optimize classification accuracy while minimizing computational requirements, ensuring efficiency in real-world applications. Results: Among the tested models, Xception emerged as the top performer, achieving a weighted accuracy of 98.73% and a weighted F1 score of 95.29%, demonstrating exceptional generalization capabilities. These models proved particularly effective in addressing class imbalances and delivering consistent performance across various evaluation metrics, thus demonstrating their suitability for clinical adoption. However, challenges persist in improving recall for the Glioma and Meningioma categories, and the black-box nature of deep learning models requires further attention to enhance interpretability and trust in medical settings. Conclusions: The findings underscore the transformative potential of deep learning in medical imaging, offering a pathway toward more reliable, scalable, and efficient diagnostic tools. Future research will focus on expanding dataset diversity, improving model explainability, and validating model performance in real-world clinical settings to support the widespread adoption of AI-driven systems in healthcare and ensure their integration into clinical workflows.
背景/目的：脑肿瘤分类是医学诊断中的关键任务，早期准确检测可显著改善患者预后。本研究旨在探讨预训练深度学习模型在脑部MRI图像分类中的有效性，将图像分为胶质瘤、脑膜瘤、垂体瘤及无肿瘤四类，以通过自动化提升诊断流程效率。 方法：本研究采用包含7023张图像的公开脑肿瘤MRI数据集，运用包括Xception、MobileNetV2、InceptionV3、ResNet50、VGG16和DenseNet121在内的前沿预训练模型，结合迁移学习微调技术及先进的预处理与数据增强方法。通过迁移学习优化模型分类精度并降低计算需求，确保实际应用中的高效性。 结果：在测试模型中，Xception表现最优，加权准确率达98.73%，加权F1分数为95.29%，展现出卓越的泛化能力。这些模型在解决类别不平衡问题和保持各项评估指标一致性方面表现突出，证明了其临床应用的可行性。然而，胶质瘤和脑膜瘤类别的召回率仍有提升空间，且深度学习模型的"黑箱"特性需进一步研究以增强医学场景中的可解释性与可信度。 结论：研究结果凸显了深度学习在医学影像领域的变革潜力，为开发更可靠、可扩展且高效的诊断工具提供了路径。未来研究将聚焦于扩展数据集多样性、提升模型可解释性，并在真实临床环境中验证模型性能，以推动人工智能驱动系统在医疗领域的广泛应用，确保其与临床工作流程的有效整合。
肿瘤(癌症)患者之家