Magnetic resonance imagining-based automated brain tumor detection using deep learning techniques

Abstract

A brain tumor refers to an accumulation or growth of anomalous cells in the brain. The mass can either be benign (noncancerous) or malignant (cancerous). Cancerous brain tumors are the source of morbidity for which diagnosis and treatment require extensive resource allocation, experienced and skilled radiologists and doctors, and sophisticated diagnostic and therapeutic technology. Early detection of brain tumors is the basic requirement for the treatment of the patient. Manual detection of the brain tumor is an invasive process and hence is extremely risky. So, advancements in medical imaging techniques, such as magnetic resonance imagining (MRI), have proved to be an important tool in the early detection of brain tumors. Even with the advancements in medical imaging, it remains a very exigent task for radiologists. In many scenarios, the unavailability of a skilled radiologist or doctor can lead to improper diagnosis of the patient. Artificial intelligence and computer vision have successfully been used to achieve human-like accuracy in various image classification tasks. In this chapter, we propose various algorithms to detect the presence of brain tumors in MRI scans of the brain. We use various state-of-the-art convolutional neural networks and apply transfer learning to achieve this goal. We have also used machine learning algorithms that were trained on the embedding of the MRI scans, which were acquired through deep feature extraction, to detect the presence of abnormalities. Moreover, this chapter compares the performance of various models and techniques for automatic brain tumor detection using deep learning.