WSSOA: whale social spider optimization algorithm for brain tumor classification using deep learning technique

Brain tumors can have detrimental effects on brain function and pose a serious threat to life. Detecting and treating brain tumors early is vital for saving lives. However, identifying tumor-affected brain cells is a difficult and time-consuming process. Common imaging techniques like Computer Tomography scans and Magnetic Resonance Images (MRIs), while helpful, can present challenges for radiologists in manual assessments. The field of image processing faces significant obstacles in achieving accurate and efficient brain tumor detection. This research work proposes an improved deep learning-based model for efficient brain tumors detection. Preprocessing, segmentation, feature extraction, feature selection, and classification are some of the processes that make up the proposed model. To improve the quality of brain images, preprocessing steps are employed using the compound filter made up of Gaussian, mean, and median filters. In addition, morphological and threshold-based segmentation are used to separate the tumor from healthy brain tissue. By using the grey-level co-occurrence matrix (GLCM)-based technique is employed to extract the texture and intensity patterns for identifying tumor areas. The optimal feature selection is performed by using the Whale Social Spider-based Optimization Algorithm (WSSOA)-based metaheuristic. Finally, Deep Convolutional Neural Network (DCNN) is used for accurate tumors detection. The proposed technique is evaluated using a publicly well-known Figshare dataset. Performance is compared with seven latest state-of-art models using metrics such as accuracy, precision, recall, and F1-score. The results demonstrate that the proposed technique achieves exceptional brain tumor classification accuracy of 99.29%. These promising findings highlight the potential of the proposed model to enhance accurate and efficient brain tumor detection, ultimately leading to improve diagnosis and potentially saving more lives.