Brainnet: a deep learning network for brain tumor detection and classification

Abstract

The increased use of technology had an impact to the overall wellbeing. Health experts have increasingly taken advantage of the benefits of these technologies thus generating a scalable improvement in the area of health care. Because of this, there is paradigm shift from manual monitoring toward more accurate virtual monitoring with minimum percentage of error in the area of health care. Advances in artificial intelligence (AI) led to exciting solutions with good accuracy for medical imaging and is a key method for future applications in health care. Brain tumor detection is an important task in medical image processing. Early detection of brain tumors plays an important role in improving treatment possibilities and thus increasing the survival rate of the patients. Manual detection of the brain tumors for cancer diagnosis from a large amount of MRI images generated in clinical routines is a difficult and time consuming task due to the complexity and variance of tumors and medical data. So, there is a need for automatic brain tumor detection from brain MRI images. With the help of deep learning networks, we can automate the detection process. For that, we have proposed a new network known as BrainNET which reads the MRI images coming from the MRI machine, and then, it detects as well as classifies the brain tumor if present.