An Efficient Deep Learning based Approach for the Detection of Brain Tumors

ABSTRACT

Deep learning has stretched out its roots even more in our daily lives. As a society, we are witnessing small changes in lifestyle such as self-driving cars, Google Assistant, Netflix recommendations, and spam email detection. Similarly, deep learning is also evolving in healthcare, and today many doctors often use it more comfortably. Using deep learning models we can detect severe brain tumors with the help of MRI scans, in fact in the Covid era, deep learning evolved majorly to detect the disease with the help of Lung X-Rays. Magnetic Resonance Imaging (MRI) is used when a person has a brain tumor to detect it. Brain tumors can fall into any category, and MRI scans of these millions of people are needed to determine if they have the disease and if so, which category they belong to. Determining the type of brain tumor can be a rigid task and deep learning models play an important role here. For the proposed deep learning model, we have implemented convolution neural networks (CNN) through which our model has achieved a testing accuracy of 96.5%. Also, along with this, the libraries of Keras and Tensorflow have been explored by the authors in this research. © 2022 IEEE.