Brain Tumour Detection using Sequential Recurrent Neural Network through Optimal Two Phase Selected Features

Abstract

In human body, the brain is a complex organ with billions of cells that controls the overall activities of our body. The abnormal and uncontrolled multiplication of these cells leads to Brain tumour which is one of the most dangerous and aggressive second leading diseases in human beings around the world.  Many Machine Learning algorithms are used with different classifiers such as Discrete Wavelet Transform, Support Vector Machine, Principal Component Analysis, etc., on Magnetic Resonance (MR) images to segment and classify tumour types. These methods are used only to predict whether the patient has brain tumour or not and also never consider the morphology and histological nature of brain tumour types. This method provides accuracy based on MR image quality, features, contrast, brightness etc. These methods show upto 95-97% of accuracy. In the next decades, Deep Learning (DL) architectures such as Deep Belief Network (DBN), Deep Neural Network (DNN), and Convolutional Neural Network (CNN) are introduced used and they are mainly focused on the fields as image recognition, natural processing, video recognition, etc. The CNN model used to recognize the tumour upto 96% of accuracy. To enhance the accuracy in brain tumour detection, a Sequential Recurrent Neural Network (SRNN) model is implemented in which instead of MR images, symptoms are considered to predict brain tumour. These symptoms are obtained through Optimal Two Phase Feature Selection (OTPFS) technique. The proposed SRNN-OTPFS model is compared with machine learning algorithms with morphology and histology fixed as a target feature. The proposed model increases the accuracy of 2 % compared to the machine learning model in both morphological and histological aspects.