Generative Intelligence‐Based Federated Learning Model for Brain Tumor Classification in Smart Health

This study presents a sophisticated ResNet-10 convolutional neural network model that is specifically developed to address the classification difficulties of brain computed tomography (CT) images, particularly those associated with Alzheimer's disease (AD), brain lesions (including tumors), and normal aging in smart healthcare. The model employs a residual hybrid attention module (RHAM) to enhance the specificity of features, enabling it to effectively collect both spatial information and relevant content within brain tissue. These enhancements enhance the model's efficacy in both traditional categorization and brain tumor diagnosis through the utilization of associative learning and interpretable generative artificial intelligence (GAI). To streamline the intricacy of the design, a global media collecting layer is implemented, and a dropout mechanism is incorporated in the subsequent levels to prevent unnecessary installation. Throughout training, this model makes use of label smoothing entropy loss functions to enhance its capacity for generalization, even with a limited quantity of training samples. The advanced ResNet-10 network model has been extensively tested and proven effective on brain CT scans, obtaining an incredible 97.47% classification accuracy. The demonstration emphasized its potential application in broader domains such as GAI-based collaborative learning and brain tumor detection.

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