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• W4360851195 endingPage "1229" @default.
• W4360851195 startingPage "1229" @default.
• W4360851195 abstract "Brain tumors are nonlinear and present with variations in their size, form, and textural variation; this might make it difficult to diagnose them and perform surgical excision using magnetic resonance imaging (MRI) scans. The procedures that are currently available are conducted by radiologists, brain surgeons, and clinical specialists. Studying brain MRIs is laborious, error-prone, and time-consuming, but they nonetheless show high positional accuracy in the case of brain cells. The proposed convolutional neural network model, an existing blockchain-based method, is used to secure the network for the precise prediction of brain tumors, such as pituitary tumors, meningioma tumors, and glioma tumors. MRI scans of the brain are first put into pre-trained deep models after being normalized in a fixed dimension. These structures are altered at each layer, increasing their security and safety. To guard against potential layer deletions, modification attacks, and tempering, each layer has an additional block that stores specific information. Multiple blocks are used to store information, including blocks related to each layer, cloud ledger blocks kept in cloud storage, and ledger blocks connected to the network. Later, the features are retrieved, merged, and optimized utilizing a Genetic Algorithm and have attained a competitive performance compared with the state-of-the-art (SOTA) methods using different ML classifiers." @default.
• W4360851195 created "2023-03-25" @default.
• W4360851195 creator A5004401480 @default.
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• W4360851195 creator A5060868363 @default.
• W4360851195 date "2023-03-24" @default.
• W4360851195 modified "2023-09-26" @default.
• W4360851195 title "Blockchain-Based Deep CNN for Brain Tumor Prediction Using MRI Scans" @default.
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• W4360851195 doi "https://doi.org/10.3390/diagnostics13071229" @default.
• W4360851195 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37046446" @default.
• W4360851195 hasPublicationYear "2023" @default.
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