What type of study is this?

This is a Validation Study study.

December 2, 2025

Multiclass Brain Tumor Detection with Attention-Embedded CNN Framework: Advancing Toward Decentralized Deep Learning-Based Health Monitoring

Key Points

Accurate diagnosis of brain tumors achieved using deep learning, and CNN outperforms prior models with high testing accuracy.
The model reached maximum 98.70% testing accuracy through innovative architecture incorporating attention mechanisms and residual connections.
Assessment involved online brain tumor datasets including glioma and meningioma, with rigorous 10-fold cross-validation applied.
Highlights the potential role of this model in enhancing clinical decision-making and the importance of federated learning for secure medical data integration.

Abstract

In the domain of modern healthcare research, decentralized learning based computer-aided diagnosis (CAD) is transformative for health monitoring and disease detection, particularly in diagnosing life-threatening conditions like brain tumors, where accurate and prompt identification is crucial for effective treatment. Decentralized learning-CAD systems address the challenge of secure integration of heterogeneous medical datasets that greatly affects the performance of automated diagnostic models. Brain tumors are complex and life-threatening forms of cancer, and their diagnosis heavily depends on the expertise of radiologists. Deep learning-based automated tumor classification models like convolutional neural networks (CNN), can significantly aid healthcare professionals and speed-up the process effectively. The proposed model is a custom CNN that incorporates inception blocks with attention mechanisms and residual connections, improving its ability to capture intricate patterns within medical images. The model was trained and validated on the online brain tumor datasets of MR images divided into four categories: glioma, meningioma, pituitary tumor, and a class without tumor. The custom CNN proposed achieves a maximum of 98.70% testing accuracy and outperformed existing deep learning-based classification models. Further, a 10-fold cross-validation was performed, yielding an average testing accuracy of 98.23%. In federated learning setup, the globally aggregated model was evaluated on testing data images, and the proposed model also achieved testing accuracy of 98.33%. These results underline the effectiveness of the proposed architecture in accurately detecting and classifying brain tumors, reinforcing its potential as a supportive tool for clinical decision-making.

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