Advancing multi-categorization and segmentation in brain tumors using novel efficient deep learning approaches

A brain tumor is the development of abnormal brain cells, some of which may progress to cancer. Early identification of illnesses and development of treatment plans improve patients' quality of life and life expectancy. Brain tumors are most commonly detected by magnetic resonance imaging (MRI) scans. The range of tumor sizes, shapes, and locations in the brain makes the existing approaches inadequate for accurate classification. Furthermore, using the current model takes a lot of time and yields results that are not as accurate. The primary goal of the suggested approach is to categorize whether a brain tumor is present, identify its type and divide the affected area into segments.

Therefore, this research introduced a novel efficient DL-based extension residual structure and adaptive channel attention mechanism (ERSACA-Net) to classify the brain tumor types as pituitary, glioma, meningioma and no tumor. Extracting features in brain tumor analysis helps in accurately characterizing tumor properties, which aids in precise diagnosis, treatment planning, and monitoring of disease progression. For this purpose, we utilized Enhanced Res2Net to extract the essential features. Using the Binary Chaotic Transient Search Optimization (BCTSO) Algorithm, the most pertinent features in terms of shape, texture, and colour are chosen to minimize complexity.

Finally, a novel LWIFCM_CSA approach is introduced, which is the ensemble of Local-information weighted intuitionistic Fuzzy C-means clustering algorithm (LWIFCM) and Chameleon Swarm Algorithm (CSA). Conditional Tabular Generative Adversarial Network (CTGAN) is used to tackle class imbalance problems. While differentiating from existing approaches, the proposed approach gains a greater solution. This stable improvement in accuracy highlights the suggested classifier's strong performance and raises the possibility of more precise and trustworthy brain tumor classification. In addition, our method's processing time, which averaged 0.11 s, was significantly faster than that of previous approaches.