Brain tumor diagnosis from MR images using boosted multi-gradient support vector machine classifier

A brain tumor develops as a result of uncontrolled and rapid cell proliferation. If not treated in its early stages, it might result in death. Despite several significant efforts and positive outcomes, accurate segmentation and classification remain challenging jobs. The variations in tumor size, shape, and location provide a substantial challenge for brain tumor diagnosis. Therefore, identifying brain tumors manually is challenging, time-consuming, and prone to mistakes. Consequently, there is now a need for high-accuracy automated computer-assisted diagnostics. This paper proposes a novel brain tumor detection method based on a machine learning classifier. Initially, the brain tumor images are collected from the "Magnetic Resonance Imaging (MRI)" database. In the preprocessing stage, anisotropic filtering and "Adaptive Histogram Equalization (AHE)" are performed to remove the noise and enhance the image contrast respectively. Then the images are segmented using "Enhanced Fruitfly Optimization-based Otsu segmentation (EFO-OTSU)". The feature extraction is done using "Principal Component Analysis (PCA)" and "Discrete Wavelet Transform (DWT)". We propose Boosted "Multi-Gradient Support Vector Machine (BMG-SVM)"to use the retrieved characteristics to divide the picture into the tumor and non-tumor sections. Further to enhance the classification performance, we employ the "Black Monkey Optimization (BMO)" algorithmalgorithm. A few currently used approaches are contrasted with the simulation results of the suggested technique. The final findings show that the suggested technique outperforms the other methods in terms of effectiveness.