Enhanced Brain Tumor Detection Using DCGAN Augmentation and Optimized EfficientDet in IoT-Based Healthcare Industry 5.0

This research proposes a solution integrating AIGC technology with optimized object detection networks to address challenges in brain tumor identification under the context of Medical Industry 5.0. First, to mitigate data scarcity, DCGAN is employed to augment the Br35H dataset by generating high-quality synthetic samples, enhancing model generalization. Second, a hierarchical feature-enhanced EfficientDet (EfficientDet-HFE) model is developed by combining FPN and EfficientDet architectures, fusing high-level semantic information with low-level spatial details to optimize feature transmission pathways. Additionally, the SimAM is introduced, integrated with a global-local feature optimization strategy to construct a recursive attention module. As BiFPN iterates progressively, the capacity for key region feature expression and the efficiency of multi-scale feature extraction are significantly enhanced. In response to the computational resource constraints of medical devices, LAMP techniques are applied to compress the network structure. With the assistance of fine-tuning strategies, the model parameters are reduced to 32.20 MB while preserving detection performance. Experimental results demonstrate that this method achieves 92.25% recall, 93.36% precision, 92.80% F1 Score, and 94.99% mAP on the augmented dataset, validating its efficacy in tumor detection. This research offers a lightweight, IoT-compatible solution for brain tumor detection, promoting the integration of AI-driven diagnostics into Healthcare Industry 5.0 ecosystems.