BRAIN TUMOR DETECTION USING DEEP LEARNING TECHNIQUE

Abstract

Artificial intelligence technology is making a growing influence in the healthcare industry. Growth of abnormal cells or lump in your brain is known as a brain tumour. Many different types of brain tumours exist. Primarily brain tumors can be classified into benign (non -cancerous) and malignant (cancerous). According to a random-effects model, the total incidence rate of all brain cancers is 10.82 (95% CI: 8.63-13.56) per 100 000 person-years. The most aggressive and potentially fatal brain tumours, gliomas grow extremely quickly. Due to their uneven shape and diffused borders with the surrounding area, gliomas might be difficult to segment using computer-aided diagnostics. A lot of work is being done to improve the effectiveness of Brain tumour Detection in the early stages. The most popular technique for visualising important brain areas is magnetic resonance imaging (MRI). MRI processing is one of the components of image processing, which has recently experienced the largest growth. The growth Detection is frequently the first stage. This study explains the thresholding method for brain tumour identification. The suggested method can effectively be used to recognise and isolate brain tumours in MRI images. It functions as a useful tool for doctors who practise in this area. In this paper, a deep learning-based technique for segmenting brain tumours from several MRI modalities is given.