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- W4387162023 abstract "Brain tumor is a life-threatening disease that can disrupt normal brain functioning and have a significant impact on a patient’s quality of life. Early detection and diagnosis are crucial for effective treatment. In recent years, deep learning techniques for image analysis and detection have played a vital role in the medical field, supplying more accurate and reliable results. Segmentation, the process of distinguishing between normal and abnormal brain cells or tissues, is a critical step in the detection of brain tumors. In this research, we aim to investigate various techniques for brain tumor detection and segmentation using Magnetic Resonance Imaging (MRI) images. The detection process begins by analyzing the symmetric and asymmetric shape of the brain to identify abnormalities. We will then classify the cells as either Tumored or non-Tumored. This research is aimed at finding a more accurate and efficient method for detecting brain tumors. Four Keras models are compared side by side to find out the best deep learning model for providing a suitable outcome. The models are ResNet50, DenseNet201, Inception V3 and MobileNet. These models gave training accuracy of 85.30%, 78%, 78%, and 77.12% respectively." @default.
- W4387162023 created "2023-09-30" @default.
- W4387162023 creator A5076415575 @default.
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- W4387162023 date "2023-04-21" @default.
- W4387162023 modified "2023-10-12" @default.
- W4387162023 title "Brain Tumor Detectin Using Deep Learning Model" @default.
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- W4387162023 doi "https://doi.org/10.1109/inc457730.2023.10262967" @default.
- W4387162023 hasPublicationYear "2023" @default.
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