Abstract
In the planning and quantitative assessment of brain tumor treatment, determining the tumor extent is a major challenge. Non-invasive magnetic resonance imaging (MRI) has developed as a diagnostic technique for brain malignancies without the use of ionizing radiation. Gliomas (tumors) are the most common and severe kind of brain tumor due to their infiltrative nature and rapid growth. Identifying tumor boundaries from healthy cells in the clinical environment is still a challenging task. The fluid-attenuated inversion recovery (FLAIR) MRI method can provide clinicians with information on tumor infiltration. Many recommendations include using deep neural networks (DNN) in image segmentation because they perform well in automated brain tumor image segmentation. Due to the complexity of the gradient diffusion problem, training a deeper neural network requires a lot of time and a lot of computing resources. As a result, utilizing fluid-attenuated inversion recovery (FLAIR) MRI data, this project compares two deep learning architectures, U-Net, ResNet, AlexNet, VGG16-Net, and V-Net, for totally automated brain lesion diagnosis and segmentation. In contrast to traditional supervised machine learning techniques, these deep learning-based algorithms do not rely on natural features, and instead, construct a pyramid increasingly complex characteristics are drawn directly from the data.
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Chatterjee, P., Sahoo, S., Kar, S., Biswal, P. (2022). Comparative Study of Medical Image Segmentation Using Deep Learning Model. In: Dehuri, S., Prasad Mishra, B.S., Mallick, P.K., Cho, SB. (eds) Biologically Inspired Techniques in Many Criteria Decision Making. Smart Innovation, Systems and Technologies, vol 271. Springer, Singapore. https://doi.org/10.1007/978-981-16-8739-6_27
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