BRAIN TUMOR DETECTION FROM MRI IMAGES USING DISCRETE COSINE TRANSFORM FEATURES AND EXTREME LEARNING MACHINE

Abstract

A brain tumor is an abnormal growth of brain tissue and characterized by excessive cell proliferation in certain parts of the brain. One of the current, reliable technologies that can be used to identify brain tumors is Magnetic Resonance Imaging (MRI) scans. The scanned MRI images are then conventionally monitored and examined by a specialist for the presence of tumors. As the number of people suffering from brain tumors is significantly increasing and their corresponding mortality rate has reached 18,600 by 2021, research on designing more effective and efficient tools to assist medical specialists in identifying brain tumors is considered of great importance. In a previous study, a machine learning-based model demonstrated its ability to detect brain tumors with a classification accuracy of 92%. Several hyperparameters were computationally tested using public MRI datasets to obtain the most reliable detection/binary classification accuracy on MRI brain images. Sophisticated model accuracy was achieved by testing various neuronal units and ELM activation functions, followed by inserting a feature map extracted from the Discrete Cosine Transform (DCT). The model obtained the highest testing accuracy of 95% with several 20 ELM neuron units with a tanh activation function.