Brain Tumor Detection using Improved Binomial Thresholding Segmentation and Sparse Bayesian Extreme Learning Machine Classification

Abstract

People are dying these days from numerous deadliest diseases. One such illness is brain tumour, in which the unusual cells within the tumour quickly begin to damage the brain's healthy cells. Owing to this rapid growth, a person may pass away before the disease receives a correct diagnosis. Early disease detection is essential for any disease to help save the patient by providing them with better care. In a similar vein, a patient's life depends on early brain tumour detection. Brain tumour detection is an extremely challenging procedure that we would like to simplify in order to save time. The proposed model facilitates the quicker and more accurate identification of abnormal brain cells, leading to the early detection of brain tumours. In this work, an improved binomial thresholding-based segmentation (IBTBS) is introduced for segmentation purpose. From this segmented image, information theoretic based, wavelet transform (WT) based, and wavelet scattering transform (WST) based features are extracted. An optimization-based feature selection approach (OBFSA) is incorporated between feature selection and tumour classification in order to reduce the dimension of this retrieved feature. Finally, classification is performed using the Sparse Bayesian extreme learning machine (SBELM) classifier. The execution process of this proposed methodology takes an MRI image from the free accessible source. By computing and detecting four different parameters, the experimental analysis of the proposed approach displays the accuracy, specificity, and sensitivity values. This model can assist us in quickly diagnosing brain tumours, potentially saving the lives of patients.