Deep Learning-Based Algorithm for Identifying and Detecting Brain Glioma Tumors

The human brain is considered the most complex region of the body because it used to have biological elements and a number of neurons. When assessing its potential functionality, the investigation found that a healthy human's normal brain typically functions at 93\% of its potential. A brain tumour occurs when aberrant brain cells proliferate. Tumours can be classified into two categories: malignant and benign (non-cancerous). These are further classified into primary tumours, which start inside the brain, and secondary tumours, also known as brain metastasis tumours, which often spread from diseases outside the brain. The early discovery of malignant cells significantly impacts the treatment and patient recovery. On the other hand, diagnosing the disease typically goes through a lengthy, complicated procedure where a lot of money is also required. One of the most valuable approaches is MRI imaging, using which we can identify the various stages for the detection of tumours. Here, For the purpose of detecting brain tumours from MRI images, numerous feature extraction and classification techniques are also available. In this paper, the Convolutional Neural Network approach is used to detect early pituitary gland-based tumour disease in humans.