Robust brain tumor detection and classification via multi-technique image analysis

Accurate detection and classification of brain tumors play a critical role in neurological diagnosis and treatment.Proposed work developed a sophisticated technique to precisely identify and classify brain neoplasms in medical imaging. Our approach integrates various techniques, including Otsu's thresholding, anisotropic diffusion, modified 3-category Fuzzy C-Means (FCM) for segmentation after skull stripping and wavelet transformation for post-processing for segmentation, and Convolution neural networks for classification. This approach not only recognizes that discriminating healthy brain tissue from tumor-affected areas is challenging, yet it also focuses on finding abnormalities inside brain tumors and early detection of tiny tumor structures. Initial preprocessing stages improve the visibility of images and the identification of various regions while accurately classifying tumor locations into core, edema, and enhancing regions by segmentation as well. Ultimately, these segmented zones are refined using wavelet transforms, which remove noise and improve feature extraction. Our CNN architecture uses learned abstractions to distinguish between healthy and malignant regions, ensuring robust classification. It is particularly good at identifying tiny tumors and detecting anomalies inside tumor regions, which provides substantial advances in accurate tumor detection. Comprehensive hypothetical evaluations validate its efficacy, which could improve clinical diagnostics and perhaps influence brain tumor research and treatment approaches.