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- W4387194144 abstract "A brain tumor puts one’s life in danger and interferes with the body’s regular operations. If a brain tumor is correctly predicted, people’s lives can be saved; if the prediction is incorrect, people could die. Clinical specialists must complete a complex and lengthy procedure called brain illness prediction, and their performance is based on their level of competence. In order to successfully treat the patient, the earlier prediction offers information about the patient’s brain status. Deep learning (DL) algorithms can be used to resolve complicated and nonlinear situations. The proposed solution used Kaggle dataset, which consists of 253 brain MRI images, 155 of which are said to include tumors. Data Augmentation and Histogram Equalization can be utilized to effectively find the Brain Tumor. Various deep learning (DL) methods, such as VGG-19, can be used to improve classification and prediction accuracy. In this work, we examined the accuracy of the CNN model both before and after histogram equalization with VGG-19. It has been demonstrated that the new models are much more accurate than the ones currently in use. In our research, VGG-19 achieved accuracy scores of 99.99% for training, 98.45% for testing, and 98.44% for validation." @default.
- W4387194144 created "2023-09-30" @default.
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- W4387194144 date "2023-04-21" @default.
- W4387194144 modified "2023-09-30" @default.
- W4387194144 title "Brain tumor detection using histogram equalization and deep learning" @default.
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- W4387194144 doi "https://doi.org/10.1109/inc457730.2023.10263104" @default.
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