Brain tumors are prevalent and aggressive disease, with a relatively short life expectancy in their most severe form. Thus, treatment planning is an important element in improving patient quality of life. In general, image techniques such as computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound imaging are used to examine tumors in the brain, lung, liver, and breast. MRI scans are used in this study to diagnose brain tumors. As a result, a reliable and automated classification technique is required to prevent death. Automatic brain tumor detection using convolutional neural networks (CNN) classification is proposed in this chapter. Small kernels are used to conduct the deeper architectural design. In machine learning, brain tumor classification is done by using a binary classifier to detect brain tumors from MRI scan images. In this chapter, transfer learning is used to build the classifier, achieving a good accuracy and visualizing the model's overall performance.
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