Abstract
Artificial Intelligence has the potential to bring about an exemplary reposition in the detection of brain tumors. Many health organizations have identified brain tumors as the second leading cause of mortality in humans worldwide. The possibility of an effective medical therapy exists if a brain tumor is identified at an early stage. For appropriate diagnosis, Magnetic Resonance Imaging (MRI) is firmly recommended for individuals with brain tumor indications. The immense geographical and structural variety of the brain tumor’s surrounding environment makes automatic brain tumor classification a challenging task. The differences in the tumor site, structure, and size present a significant difficulty for brain tumor identification. This research proposes the design and implementation of Convolutional Neural Networks (CNN) classification for enabling automatic brain tumor detection. When compared to other cutting-edge methodologies such as Support Vector Machines (SVM) and Deep Neural Networks (DNN), obtained results demonstrate that CNN repositories have a rate of 97.5% accuracy with minimal intricacy.
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Ifra, A.B., Sadaf, M. (2023). Automatic Brain Tumor Detection Using Convolutional Neural Networks. In: Bhateja, V., Sunitha, K.V.N., Chen, YW., Zhang, YD. (eds) Intelligent System Design. Lecture Notes in Networks and Systems, vol 494. Springer, Singapore. https://doi.org/10.1007/978-981-19-4863-3_41
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DOI: https://doi.org/10.1007/978-981-19-4863-3_41
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