Abstract
An intracranial tumor is another name for a brain tumor, is a fast cell proliferation and uncontrolled bulk of tissue, and seems unaffected by the mechanisms that normally govern normal cells. The identification and segmentation of brain tumors are among the most common difficult and time-consuming tasks when processing medical images. MRI is a medical imaging technique that allows radiologists to see within body structures without requiring surgery. The information provided by MRI regarding human soft tissue contributes to the diagnosis of brain tumors. In this paper, we use several Convolutional Neural Network architectures to identify brain tumor MRI. We use a variety of pre-trained models such as VGG16, VGG19, and ResNet50, which we have found to be critical for reaching competitive performance. ResNet50 performs with an accuracy of 96.76% among all the models.
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Bitto, A.K., Bijoy, M.H.I., Yesmin, S., Mia, M.J. (2023). Tumor-TL: A Transfer Learning Approach for Classifying Brain Tumors from MRI Images. In: Satu, M.S., Moni, M.A., Kaiser, M.S., Arefin, M.S. (eds) Machine Intelligence and Emerging Technologies. MIET 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 490. Springer, Cham. https://doi.org/10.1007/978-3-031-34619-4_15
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DOI: https://doi.org/10.1007/978-3-031-34619-4_15
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