DEEP LEARNING-BASED BRAIN TUMOR DETECTION

Abstract

Brain tumour segmentation is an especially difficult but important task in medical imaging. This is due to the possibility of incorrect diagnosis and prognosis resulting from manual categorization. When dealing with large amounts of data, it also takes a lot of labour. Photographically, distinguishing between a brain tumour and normal tissue can be challenging due to their comparable looks and vast range of characteristics. In this study, brain tumours were extracted from two-dimensional MRI images using fuzzy C-Means. Classical classifiers and CNN were then applied. The dataset used in the study contained tumours of different sizes, forms, and intensities. SVM, KNN, MLP, LR, Naive Bayes, Random Forest, and other well known machine learning algorithms were employed in the standard classifier of the scikit-learn module.