BioSwarmNet: A Revolutionary Approach to Brain Tumour Detection Using Fractional Order Differential Particle Swarm Optimisation and Recurrent Neural Networks

Brain tumours are a major public health concern, and early and accurate detection is critical in treatment.Early and precise detection of brain tumors is paramount, yet current technologies often struggle to achieve the necessary level of accuracy due to inherent limitations in image processing and classification methodologies.While approaches like convolutional neural networks and optimization techniques have shown promise, they often fall short in capturing intricate patterns and textures or achieving sufficient sensitivity, emphasizing the need for more advanced and integrated solutions like the proposed BioSwarmNet model.The system includes a meticulously designed image processing pipeline that ensures data consistency and quality.BioSwarmNet, a novel combination of Fractional Order Differential Particle Swarm Optimisation (FODPSO) and Recurrent Neural Networks (RNNs), uses swarm intelligence and deep learning to revolutionize medical image classification.Using the well-known BRATS dataset, this study provides a promising avenue for improving diagnostic accuracy and efficiency in brain tumour detection, which has the potential to benefit both healthcare professionals and patients.Notably, the proposed system outperformed in key metrics such as 99.12% accuracy, 98.62% sensitivity, and 99.86% specificity.