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 extremely time-consuming and troublesome for physicians. Early and precise brain tumour identification effectively results and leads to an increased survival rate. This paper examines various techniques in order of priority to classify clinical images 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 difficult 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 (KMC) 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, the CNN model accomplished the highest accuracy of 99% for two sets of data: Brain Tumour Segmentation (BTS) and BD-brain tumour and high average susceptibility of 0.99 for all datasets. Finally, the review demonstrated that improving image order strategies regarding the accuracy, sensitivity value, and feasibility of Computer-Aided Diagnosis (CAD) is a significant challenge and an open research area.