A Comparison and Survey on Brain Tumour Detection Techniques Using MRI Images

Despite enormous advances in medical technology, the prognosis of Brain Tumour (BT) remains an extremely time-consuming and troublesome assignment for physicians. Early and precise brain tumour identification gives an effective results and leads to increased survival rate. Within this paper, an examination of various techniques in order of priority to classify clinical images is presented to analyse various research gaps and highlights their costs and benefits. Human mortality can be reduced by using an automatic classification scheme. The automatic classification of brain tumours is a difficult task due to the large spatial and structural variability of the brain tumor’s surrounding region. The latest developments have been investigated in image characterization strategies for diagnosing human body disease and addressing the classification of nuclear medical imaging identification techniques like Convolution Neural Network (CNN), Support Vector Machine (SVM), Histogram technique, K-Means Clustering (K-MC) etc., just as the respective parameters like the image modalities employed, the dataset and the trade-offs have been compared for each technique. Among these techniques, CNN model accomplished the highest accuracy of 99% for two sets of data: Brain Tumour Segmentation (BTS) and BD-brain tumour and a high average susceptibility of 0.99 for all datasets. Finally, the review demonstrated that improving image order strategies with regarding accuracy, sensitivity value, and feasibility for Computer-Aided Diagnosis (CAD) is a significant challenge as well as an open research area.