⚡ Quick Summary
A new study introduces the Aniso-ResCapHGBO-Net, a federated learning-based framework for detecting brain tumors from CT scans while ensuring patient privacy. This innovative approach achieved an impressive 99.07% accuracy and demonstrates the potential for decentralized medical imaging solutions. 🧠
🔍 Key Details
- 📊 Dataset: CT scans for brain tumor detection
- 🧩 Features used: ResNet-50 and capsule networks for feature extraction
- ⚙️ Technology: Aniso-ResCapHGBO-Net framework, Python, TensorFlow, PyTorch
- 🏆 Performance: 99.07% accuracy, 98.54% precision, 99.82% sensitivity
- 🔒 Security: Updates secured on the Ethereum network with SHA-256 hashing
🔑 Key Takeaways
- 🧠 Brain tumor detection is critical for early diagnosis and treatment.
- 🔒 Privacy-preserving methods are essential in handling sensitive medical data.
- 🌐 Federated learning allows multiple institutions to collaborate without sharing raw data.
- 🏆 The Aniso-ResCapHGBO-Net framework combines advanced neural networks for improved accuracy.
- 📈 High performance metrics indicate the model’s reliability in clinical settings.
- 🔗 Blockchain technology enhances data security and integrity in model updates.
- 💡 This approach reduces false negatives and false positives in tumor detection.
- 🛠️ Preprocessing techniques include anisotropic diffusion filtering and morphological operations.
📚 Background
The detection of brain tumors through medical imaging is a vital aspect of healthcare, as early and accurate diagnosis can significantly improve patient outcomes. However, traditional deep learning models often face challenges related to data privacy and compliance with regulations, especially when data is pooled from multiple healthcare institutions. This study addresses these concerns by proposing a decentralized framework that prioritizes patient privacy while maintaining high diagnostic accuracy.
🗒️ Study
The research team developed the Aniso-ResCapHGBO-Net framework, which integrates ResNet-50 and capsule networks to enhance feature extraction from CT scans. The study utilized a federated learning approach, allowing various healthcare institutions to collaborate on model training without compromising patient data privacy. The model was built using popular machine learning libraries such as Python, TensorFlow, and PyTorch.
📈 Results
The framework demonstrated remarkable performance, achieving a 99.07% accuracy, 98.54% precision, and 99.82% sensitivity in detecting brain tumors from benchmark CT imaging. These results indicate a high level of reliability in the model’s ability to identify tumors accurately while minimizing false diagnoses.
🌍 Impact and Implications
The implications of this study are profound, as it showcases a viable solution for integrating privacy-preserving technologies in medical imaging. By leveraging federated learning and blockchain technology, healthcare institutions can collaborate more effectively while ensuring patient data remains secure. This approach not only enhances diagnostic accuracy but also paves the way for broader applications of AI in healthcare, potentially transforming how medical imaging is conducted globally. 🌐
🔮 Conclusion
The introduction of the Aniso-ResCapHGBO-Net framework marks a significant advancement in the field of brain tumor detection. By combining cutting-edge technologies with a focus on patient privacy, this study highlights the potential for decentralized systems to revolutionize medical imaging. As we continue to explore the integration of AI in healthcare, the future looks promising for improved diagnostic tools that prioritize both accuracy and patient confidentiality. 🚀
💬 Your comments
What are your thoughts on this innovative approach to brain tumor detection? We would love to hear your insights! 💬 Leave your comments below or connect with us on social media:
A federated learning-based privacy-preserving image processing framework for brain tumor detection from CT scans.
Abstract
The detection of brain tumors is crucial in medical imaging, because accurate and early diagnosis can have a positive effect on patients. Because traditional deep learning models store all their data together, they raise questions about privacy, complying with regulations and the different types of data used by various institutions. We introduce the anisotropic-residual capsule hybrid Gorilla Badger optimized network (Aniso-ResCapHGBO-Net) framework for detecting brain tumors in a privacy-preserving, decentralized system used by many healthcare institutions. ResNet-50 and capsule networks are incorporated to achieve better feature extraction and maintain the structure of images’ spatial data. To get the best results, the hybrid Gorilla Badger optimization algorithm (HGBOA) is applied for selecting the key features. Preprocessing techniques include anisotropic diffusion filtering, morphological operations, and mutual information-based image registration. Updates to the model are made secure and tamper-evident on the Ethereum network with its private blockchain and SHA-256 hashing scheme. The project is built using Python, TensorFlow and PyTorch. The model displays 99.07% accuracy, 98.54% precision and 99.82% sensitivity on assessments from benchmark CT imaging of brain tumors. This approach also helps to reduce the number of cases where no disease is found when there is one and vice versa. The framework ensures that patients’ data is protected and does not decrease the accuracy of brain tumor detection.
Author: [‘Al-Saleh A’, ‘Tejani GG’, ‘Mishra S’, ‘Sharma SK’, ‘Mousavirad SJ’]
Journal: Sci Rep
Citation: Al-Saleh A, et al. A federated learning-based privacy-preserving image processing framework for brain tumor detection from CT scans. A federated learning-based privacy-preserving image processing framework for brain tumor detection from CT scans. 2025; 15:23578. doi: 10.1038/s41598-025-07807-8