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- W3206120112 abstract "Brain tumors are among the most aggressive of common diseases and can lead to drastic reduction of the lifespan of those affected—effective diagnosis and treatment planning thus become highly important. Broadly, the methods used to diagnose tumors in the brain are computed tomography scan, magnetic resonance imaging scan and ultrasound imaging. Brain tumor detection is a crucial and difficult task in the medical image processing field, and it requires handling large amount of data. Manual classification generally results in false prediction and diagnosis. Magnetic resonance imaging is the imaging technique used to diagnose the brain tumor. In this paper, various transfer learning models such as MobileNet, InceptionV3, ResNet50 and VGG19 are applied to train the model to detect brain tumors from magnetic resonance images and compare these methods. The above models are trained on the BraTS 2015 dataset and observed accuracy rates of 90.54%, 85.96%, 95.42% and 91.69%, respectively.KeywordsBrain TumorMagnetic Resonance ImagesNeural NetworkData AugmentationTransfer Learning" @default.
- W3206120112 created "2021-10-25" @default.
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- W3206120112 date "2021-10-09" @default.
- W3206120112 modified "2023-09-25" @default.
- W3206120112 title "Detection of Brain Tumors—A Comparative Analysis of Various Transfer Learning Methods" @default.
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- W3206120112 doi "https://doi.org/10.1007/978-981-16-3675-2_14" @default.
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