Brain tumour detection via EfficientDet and classification with DynaQ-GNN-LSTM

Abstract

The early detection and accurate staging of brain tumors are critical for effective treatment strategies and improving patient outcomes. Existing methods for brain tumor classification often struggle with limitations such as suboptimal precision, accuracy, and recall rates, alongside significant delays in processing. The current methodologies in brain tumor classification frequently encounter issues such as inadequate feature extraction capabilities and limited accuracy in segmentation, which impede their effectiveness. To address these challenges, the proposed model integrates Fuzzy C-Means for segmentation, leveraging its ability to enhance the accuracy in distinguishing tumor regions. Bounding boxes surrounding identified tumour regions are produced by the method by efficiently utilising calculated region attributes. The use of Vision Transformers for feature extraction marks a significant advancement, offering a more nuanced analysis of the intricate patterns within brain imaging data samples. These features are then classified using a Dyna Q Graph LSTM (DynaQ-GNN-LSTM), a cutting-edge approach that combines the strengths of deep learning, reinforcement learning, and graph neural networks. The superiority of the proposed model is evident through its performance on multiple datasets. It demonstrates an 8,3 % increase in precision, 8,5 % increase in accuracy, 4,9 % increase in recall and 4,5 % increase in specificity, alongside 2,9 % reduction in delay compared to existing methods. In conclusion, the proposed method offers an efficient solution to the challenges faced in brain tumor classification. The study's findings underscore the transformative impact of integrating cutting-edge technologies in medical diagnostics, paving the way for more accurate, and timely health interventions for clinical scenarios