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- W3157221569 abstract "Cancers or tumors have their impact effects on humans, especially if the cancer is localized in an important organ such as the brain. It is important to detect cancer earlier so that many lives can be saved. As cancer diagnosis is highly time-consuming and needs expensive tools, there is an immediate requirement to develop non-invasive, cost-effective, and efficient tools for brain cancer staging and detection. Brain scans that are commonly used are magnetic resonance imaging (MRI) and computed tomography (CT). In this paper, we studied the common algorithms that are used for brain tumor detection using imaging modalities of brain cancer and automatic computer-assisted methods. The main objective of this paper is to make a comparative analysis of several methods of detecting tumors in the Central Nervous System (CNS). The results of the applied classifiers are compared and analyzed using different metrics including accuracy, precision, and recall. The best accuracy reached using machine learning algorithms is 85.56% accuracy with Random Forest, while the best classifier among applied deep learning algorithms is Inception V4 with 97.36%." @default.
- W3157221569 created "2021-05-10" @default.
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- W3157221569 date "2021-01-01" @default.
- W3157221569 modified "2023-09-24" @default.
- W3157221569 title "Brain Tumor Diagnosis System Based on RM Images: A Comparative Study" @default.
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- W3157221569 doi "https://doi.org/10.1007/978-3-030-70713-2_15" @default.
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