Integrating MobileNetV3 and SqueezeNet for Multi-class Brain Tumor Classification.

Sahithi Kantu, Hema Sai Kaja, Vaishnavi Kukkala, Salah A Aly, Khaled Sayed
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Abstract

Brain tumors pose a critical health threat requiring timely and accurate classification for effective treatment. Traditional MRI analysis is labor-intensive and prone to variability, necessitating reliable automated solutions. This study explores lightweight deep learning models for multi-class brain tumor classification across four categories: glioma, meningioma, pituitary tumors, and no tumor. We investigate the performance of MobileNetV3 and SqueezeNet individually, and a feature-fusion hybrid model that combines their embedding layers. We utilized a publicly available MRI dataset containing 7023 images with a consistent internal split (65% training, 17% validation, 18% test) to ensure reliable evaluation. MobileNetV3 offers deep semantic understanding through its expressive features, while SqueezeNet provides minimal computational overhead. Their feature-level integration creates a balanced approach between diagnostic accuracy and deployment efficiency. Experiments conducted with consistent hyperparameters and preprocessing showed MobileNetV3 achieved the highest test accuracy (99.31%) while maintaining a low parameter count (3.47M), making it suitable for real-world deployment. Grad-CAM visualizations were employed for model explainability, highlighting tumor-relevant regions and helping visualize the specific areas contributing to predictions. Our proposed models outperform several baseline architectures like VGG16 and InceptionV3, achieving high accuracy with significantly fewer parameters. These results demonstrate that well-optimized lightweight networks can deliver accurate and interpretable brain tumor classification.

集成MobileNetV3和SqueezeNet的多类脑肿瘤分类。
脑肿瘤是一种严重的健康威胁,需要及时准确的分类以进行有效的治疗。传统的MRI分析是劳动密集型的,容易发生变化,需要可靠的自动化解决方案。本研究探索了基于神经胶质瘤、脑膜瘤、垂体瘤和无瘤四类脑肿瘤分类的轻量级深度学习模型。我们分别研究了MobileNetV3和SqueezeNet的性能,以及结合其嵌入层的特征融合混合模型。我们使用了一个公开可用的MRI数据集,其中包含7023张图像,具有一致的内部分割(65%训练,17%验证,18%测试),以确保可靠的评估。MobileNetV3通过其表达特性提供了深入的语义理解,而SqueezeNet提供了最小的计算开销。它们的功能级集成在诊断准确性和部署效率之间建立了平衡的方法。使用一致的超参数和预处理进行的实验表明,MobileNetV3在保持低参数计数(3.47M)的同时实现了最高的测试精度(99.31%),使其适合实际部署。采用Grad-CAM可视化来提高模型的可解释性,突出肿瘤相关区域,并帮助可视化有助于预测的特定区域。我们提出的模型优于VGG16和InceptionV3等几个基线架构,用更少的参数实现了高精度。这些结果表明,经过优化的轻量级网络可以提供准确和可解释的脑肿瘤分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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