Improving healthcare sustainability using advanced brain simulations using a multi-modal deep learning strategy with VGG19 and bidirectional LSTM - Chandrasekaran, Saravanan; Aarathi, S.; Alqhatani, Abdulmajeed; Khan, Surbhi Bhatia; Quasim, Mohammad Tabrez; Basheer, Shakila (Frontiers in Medicine, 2025)
BackgroundBrain 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.MethodsThe 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.ResultsThe 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.ConclusionWith 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.