Brain cancer is the premier reason for cancer deaths all over the world. The diagnosis of brain cancer at an initial stage is mediocre, as the radiologist is ineffectual. Different experiments have been conducted and demonstrated clearly that the algorithms for nodule segmentation are unsuccessful. Therefore, the research has consolidated incremental clustering focused on superpixel segmentation as an appropriate optimization approach for the accurate segmentation of pulmonary nodules. The key aim of the research is to refine brain CT images to accurately distinguish tumors and the segmentation of small-scale anomalous nodules in the brain region. In the beginning stage, an anisotropic diffusion filters (ADF) method with un-sharp intensification masking is utilized to eliminate the noise discernment in images. In the following stage, within the improved nodule image sequence, a Superpixel Segmentation Based Iterative Clustering (SSBIC) algorithm is proposed for irregular brain tissue prediction. Subsequently, the brain nodule samples are captured using deep learning methods: Advanced Grey Wolf Optimization (AGWO) with ONN (AGWO-ONN) and Advanced GWO with CNN-based (AGWO-CNN). The proposed technique indicates that the sensitivity is increased and the calculation time is decreased. Consequently, the proposed methodology manifests that the advanced Computer-Assisted Diagnosis (CAD) system has outstanding potential for automatic brain tumor diagnosis. The average segmentation time of the nodule slice order is 1.06s, and 97% of AGWO-ONN and 97.6% of AGWO-CNN achieve the best classification reliability.

Advanced GWO with ONN (AGWO-ONN); Advanced GWO with CNN (AGWO-CNN); brain cancer; superpixel segmentation based iterative clustering (SSBIC) algorithm