Optimised brain tumour detection with the Pteropus Unicinctus optimisation reliant deep model

This research Pteropus Unicinctus optimisation (PUO) is proposed which is mathematically derived from the echolocation character of Pteropusand and the eye power searching ability of Unicinctus. The integration of convolutional neural networks (CNNs) and long short-term memory (LSTM) in the PUO-reliant deep model presents an optimal solution to enhance the precision and dependability of brain tumour detection. This strategy leverages the unique capabilities of both architectures and fine-tunes their performance through hyperparameter optimisation, which leads to better diagnostic results and improved quality of patient care in brain tumour detection. The proposed approach attained the accuracy of 99.51%, 99.58%, and 99.50%, and considering sensitivity 99.48%, 99.60%, 99.37% attained, and the model attained 99.48%, 99.58%, and 99.26% of specificity for BraTS 2019 dataset, SimBRATS 2019 dataset, and brain tumour dataset respectively.