Accurate brain tumor classification in MRI images is critical for timely diagnosis and treatment planning. While deep learning models like ResNet-18, VGG-16 have shown high accuracy, they often come with increased complexity and computational demands. This study presents a comparative analysis of effective yet simple Convolutional Neural Network (CNN) architecture and pre-trained ResNet18, and VGG16 model for brain tumor classification using two publicly available datasets: Br35H:: Brain Tumor Detection 2020 and Brain Tumor MRI Dataset. The custom CNN architecture, despite its lower complexity, demonstrates competitive performance with the pre-trained ResNet18 and VGG16 models. In binary classification tasks, the custom CNN achieved an accuracy of 98.67% on the Br35H dataset and 99.62% on the Brain Tumor MRI Dataset. For multi-class classification, the custom CNN, with a slight architectural modification, achieved an accuracy of 98.09%, on the Brain Tumor MRI Dataset. Comparatively, ResNet18 and VGG16 maintained high performance levels, but the custom CNNs provided a more computationally efficient alternative. Additionally,the custom CNNs were evaluated using few-shot learning (0, 5, 10, 15, 20, 40, and 80 shots) to assess their robustness, achieving notable accuracy improvements with increased shots. This study highlights the potential of well-designed, less complex CNN architectures as effective and computationally efficient alternatives to deeper, pre-trained models for medical imaging tasks, including brain tumor classification. This study underscores the potential of custom CNNs in medical imaging tasks and encourages further exploration in this direction.

翻译：磁共振成像（MRI）图像中准确的脑肿瘤分类对于及时诊断和治疗规划至关重要。尽管ResNet-18、VGG-16等深度学习模型已展现出高精度，但它们通常伴随着更高的复杂性和计算需求。本研究对一种高效且简单的卷积神经网络（CNN）架构与预训练的ResNet18和VGG16模型在脑肿瘤分类任务上进行了比较分析，使用了两个公开数据集：Br35H:: Brain Tumor Detection 2020和Brain Tumor MRI Dataset。尽管自定义CNN架构复杂度较低，但其性能与预训练的ResNet18和VGG16模型相比具有竞争力。在二分类任务中，自定义CNN在Br35H数据集上达到了98.67%的准确率，在Brain Tumor MRI Dataset上达到了99.62%。对于多类别分类，经过轻微架构调整的自定义CNN在Brain Tumor MRI Dataset上实现了98.09%的准确率。相比之下，ResNet18和VGG16保持了较高的性能水平，但自定义CNN提供了计算效率更高的替代方案。此外，本研究使用小样本学习（0、5、10、15、20、40和80个样本）评估了自定义CNN的鲁棒性，结果表明随着样本数量的增加，模型准确率显著提升。本研究强调了设计良好、复杂度较低的CNN架构作为更深层预训练模型的有效且计算高效的替代方案，在包括脑肿瘤分类在内的医学成像任务中的潜力。本研究进一步强调了自定义CNN在医学成像任务中的潜力，并鼓励在该方向进行更深入的探索。