Mao Yuannong, Kim Jiwook, Podina Lena, Kohandel Mohammad
Department of Applied Mathematics, University of Waterloo, Waterloo, ON, N2L 3G1, Canada.
David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, N2L 3G1, Canada.
Sci Rep. 2025 Jan 28;15(1):3596. doi: 10.1038/s41598-025-86752-y.
In the field of medical imaging, particularly MRI-based brain tumor classification, we propose an advanced convolutional neural network (CNN) leveraging the DenseNet-121 architecture, enhanced with dilated convolutional layers and Squeeze-and-Excitation (SE) networks' attention mechanisms. This novel approach aims to improve upon state-of-the-art methods of tumor identification. Our model, trained and evaluated on a comprehensive Kaggle brain tumor dataset, demonstrated superior performance over established convolution-based and transformer-based models: ResNet-101, VGG-19, original DenseNet-121, MobileNet-V2, ViT-L/16, and Swin-B across key metrics: F1-score, accuracy, precision, and recall. The results underscore the effectiveness of our architectural enhancements in medical image analysis. Future research directions include optimizing dilation layers and exploring various architectural configurations. The study highlights the significant role of machine learning in improving diagnostic accuracy in medical imaging, with potential applications extending beyond brain tumor detection to other medical imaging tasks.
在医学成像领域，特别是基于磁共振成像（MRI）的脑肿瘤分类中，我们提出了一种先进的卷积神经网络（CNN），它利用DenseNet - 121架构，并通过空洞卷积层和挤压激励（SE）网络的注意力机制进行增强。这种新颖的方法旨在改进当前最先进的肿瘤识别方法。我们的模型在一个全面的Kaggle脑肿瘤数据集上进行训练和评估，在关键指标：F1分数、准确率、精确率和召回率方面，表现优于已有的基于卷积和基于Transformer的模型：ResNet - 101、VGG - 19、原始的DenseNet - 121、MobileNet - V2、ViT - L/16和Swin - B。结果强调了我们架构增强在医学图像分析中的有效性。未来的研究方向包括优化空洞层和探索各种架构配置。该研究突出了机器学习在提高医学成像诊断准确性方面的重要作用，其潜在应用范围不仅限于脑肿瘤检测，还可扩展到其他医学成像任务。
