Sensitive Lesions and Tumour Detection in Brain using Deep Convolutional Neural Network

Abstract

A brain tumour is an abnormal development of abnormal brain cells that causes harm to the blood vessels and neurons of the human body. The two forms of brain tumours are benign and malignant. It is vital to diagnose a brain tumour early in order to save the life of someone who is in danger. According to research conducted in the United Kingdom, only approximately 15 persons out of 100 will be able to survive for ten years or longer after being recognised. Because of the advent of Artificial Intelligence, Deep Learning models are now being utilised to identify brain cancers using Magnetic Resonance Imaging (MRI) images. MRI is a type of scanning method that uses high magnetic fields and radio waves to obtain detailed pictures of the interior body. The notion of Transfer Learning and Residual Networks were employed in the suggested work. The model utilised is a classification task that uses MRI images to determine whether or not a tumour is present. We may use the same transfer learning techniques to precisely pinpoint the tumour once it has been found. We utilised several optimizers in Resnet-50 to see how our model responds to changes in optimization strategies. Our suggested model has accuracy with Amsgrad optimizer compared with Adam optimizer, demonstrating improved robustness over the state-of-the-art techniques.