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• W4379987328 abstract "The brain tumor can be made by a wild increment of irregular cells in the brain’s tissue, and it has two sorts of tumors: one is kind, and another one is a dangerous tumor. The kind brain tumor doesn’t impact the connecting customary and strong tissue, yet hazardous cancer can impact the adjoining tissues of the mind that can prompt a person’s death. Early disclosure of mind cancer can be needed to guarantee the endurance of patients. As a rule, the brain tumor is identified utilizing MRI filtering strategy. In any case, the radiologists are not giving the compelling tumor division in MRI picture due to the sporadic shape of tumors and tumor position within the brain. This article presents tumor detection in the medical data model, namely, data processing, semantic segmentation, and classification. The main goal is to present the knowledge about tumors affected by various brain image attributes through the medical database. The developed algorithms help improvise data analysis, segmentation, feature extraction, and classification. The paper concludes by evaluating the quality of the proposed semantic segmentation, and deep convolution neural network classification algorithms are used. The experimental results show that the developed proposed algorithm and medical data model are enhanced in scalability." @default.
• W4379987328 created "2023-06-10" @default.
• W4379987328 creator A5018694307 @default.
• W4379987328 date "2023-01-01" @default.
• W4379987328 modified "2023-09-24" @default.
• W4379987328 title "Artificial Intelligence for Enhancement of Brain Image Using Semantic Segmentation CNN with IoT Classification Techniques" @default.
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• W4379987328 doi "https://doi.org/10.1007/978-3-031-23602-0_26" @default.
• W4379987328 hasPublicationYear "2023" @default.
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