A Comprehensive Review on Brain Tumour Segmentation Using Deep Learning Approach

One of the most critical tasks in medical image processing is the analysis of brain tumour. Early detection of the disease however helps and assists in enhancing prior treatments and thereby increasing the survival rates of the patient. The process of conducting a manual segmentation through a series of MRI images of the brain helps to diagnose cancer. It is further accompanied through clinical routines and check-ups. The procedure of the same appears to be a tedious and time consuming job. Hence there is a need to automate the process by using advanced technologies such as machine learning and deep learning. The fundamental aim of this paper is to contribute in providing a detailed review of brain tumour segmentation methods along with the proposed method for final implementation. Automated form of brain segmentation has been widely adopted by various research scholars; wherein they use deep learning based algorithms to address the problems that remained restricted by using ML strategies. Many algorithms and techniques that fall under DL are capable to produce better results through objective evaluation on large amounts of MRI based patient data. With large and extensive research being done in the domain by using ML; this review paper differs by highlighting the trend and strategies used in DL. An introduction to brain tumours is presented in the paper followed by state-of-art algorithms which are to be proposed during the implementation phase. An assessment on current and existing work is also briefed and future directions to standardize the process of tumour segmentation are addressed.