An Approach to Detect the Presence of Neoplasm with Convolutional Neural Network
Nikunj Mahesh Wadgeri, Raju Dara · Zenodo (CERN European Organization for Nuclear Research) · 2022
The human brain is the nervous system's command centre, governing functions such as memory, vision, hearing, knowledge, personality, problem-solving capabilities, and so on. Deep learning has gained traction in almost every field where decision-making is crucial. Machine learning and deep learning algorithms in healthcare have exhibited promising results in a variety of sectors, like cancer detection with Magnetic Resonance Imaging (MRI), surgical robots, and so on. Deep learning is being employed to detect the presence of neoplasms in scanned MRI images. Neoplasm is characterized as the uncontrolled proliferation of tissues in a specificportion of the body, notably those with cancer-like traits. A brain tumour is a category of neoplasm generated by development of abnormal brain cells. Non-cancerous (benign) tumours and cancerous (malignant) tumours are the two main types of brain tumours. In accordance with the study, age plays a role in overall lifeexpectancies when a brain tumour is diagnosed. The 5-year survivalrate for people under the age of 15 is anticipated to be approximately75%. The 5-year survival rate for people aged 15 to 39 is anticipatedto be approximately 72%. The 5-year survival rate for persons aged40 and up is expected to be around 21%. Treatment for brain tumours is governed by several parameters, including that of the form of cancer, the abnormalities of the cells, and the location of the cancer in the brain. It is simple to anticipate and diagnose brain tumours at an early stage using Artificial Intelligence. Deep learning models are used to diagnose brain tumours by analysing MRI scan data. Convolutional Neural Networks (CNN) as well as other deep learning models can be used to detect tumours in scanned brain images.