Chandrasekaran Saravanan, Aarathi S, Alqhatani Abdulmajeed, Khan Surbhi Bhatia, Quasim Mohammad Tabrez, Basheer Shakila
Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, India.
Department of Computer Science and Engineering (Data Science), Dayananda Sagar College of Engineering, Bangalore, India.
Front Med (Lausanne). 2025 Apr 10;12:1574428. doi: 10.3389/fmed.2025.1574428. eCollection 2025.
Brain tumor categorization on MRI is a challenging but crucial task in medical imaging, requiring high resilience and accuracy for effective diagnostic applications. This study describe a unique multimodal scheme combining the capabilities of deep learning with ensemble learning approaches to overcome these issues.
The system integrates three new modalities, spatial feature extraction using a pre-trained VGG19 network, sequential dependency learning using a Bidirectional LSTM, and classification efficiency through a LightGBM classifier.
The combination of both methods leverages the complementary strengths of convolutional neural networks and recurrent neural networks, thus enabling the model to achieve state-of-the-art performance scores. The outcomes confirm the efficacy of this multimodal approach, which achieves a total accuracy of 97%, an F1-score of 0.97, and a ROC AUC score of 0.997.
With synergistic harnessing of spatial and sequential features, the model enhances classification rates and effectively deals with high-dimensional data, compared to traditional single-modal methods. The scalable methodology has the possibility of greatly augmenting brain tumor diagnosis and planning of treatment in medical imaging studies.
在医学成像中，基于磁共振成像（MRI）对脑肿瘤进行分类是一项具有挑战性但至关重要的任务，在有效的诊断应用中需要高弹性和准确性。本研究描述了一种独特的多模态方案，该方案将深度学习能力与集成学习方法相结合以克服这些问题。
该系统集成了三种新模态，即使用预训练的VGG19网络进行空间特征提取、使用双向长短期记忆网络（Bidirectional LSTM）进行序列依赖性学习以及通过LightGBM分类器提高分类效率。
两种方法的结合利用了卷积神经网络和循环神经网络的互补优势，从而使模型能够获得当前最优的性能分数。结果证实了这种多模态方法的有效性，其总准确率达到97%，F1分数为0.97，受试者工作特征曲线下面积（ROC AUC）分数为0.997。
与传统的单模态方法相比，该模型通过协同利用空间和序列特征提高了分类率，并有效处理了高维数据。这种可扩展的方法有可能极大地增强医学成像研究中的脑肿瘤诊断和治疗规划。
