Brain Tumor Detection Using CNN
The use of Convolutional Neural Networks (CNN) classification for automated brain tumor detection is proposed in this paper and will assist radiologists in tumor diagnosis without the need of invasive procedures.
Abstract
: Brain tumors are the most frequent and severe cancer with a life expectancy of only a few months in the most advanced stages. As a result, therapy planning is an important step in improving patients' quality of life. Various image methods such as computed tomography, magnetic resonance imaging, and ultrasound images, are commonly used to examine tumors in the brain, lung, liver, and other organs. Biopsy is used to classify brain tumors. It is done before final brain surgery. Technology advancements and machine learning can assist radiologists in tumor diagnosis without the need of invasive procedures. The convolutional neural network is a machine-learning technique that has shown to be effective in image segmentation and classification. Automatic brain tumor categorization is a difficult undertaking due to the enormous geographical and structural heterogeneity of the brain tumor's surrounding environment. The use of Convolutional Neural Networks (CNN) classification for automated brain tumor detection is proposed in this paper.