Brain Hemorrhage Detection Approach with Multi-Order Neural Networks

Authors

  • Swapnaja Amol Ubale Associate Professor, Information Technology Department, Marathwada Mitra Mandal College of Engineering Pune
  • Ashwini B. Gavali Associate Professor, Department of Computer Engineering, S. B. Patil College of Engineering, Indapur, Pune, India
  • Shital Kakad Assistant Professor Vishwakarma Institute of Technology, Pune
  • Devyani Jadhav Assistant Professor, Information Technology Department, Sanjivani College of Engineering, Kopargaon
  • Chetana Dipak Patil Assistant Professor, Electronics & Telecommunications Department, Marathwada Mitra Mandal's College of Engineering Pune

Keywords:

Brain Hemorrhage Detection, Multi-Order Neural Networks, Convolutional Neural Networks, Medical Image Analysis, Hierarchical Feature Extraction, Training Optimization, Sensitivity, Specificity, Real-time Implementation, Clinical Applications

Abstract

Brain hemorrhage poses a critical threat to patient health, demanding prompt and accurate diagnosis for effective medical intervention. In this research, we present an innovative approach to brain hemorrhage detection utilizing Multi-Order Neural Networks (MONNs). Unlike conventional Convolutional Neural Networks (CNNs), MONNs excel in capturing intricate spatial dependencies within medical images.

Our proposed model, Hemo DetectNet, incorporates multi-order convolutions to enhance the network's ability to discern subtle patterns indicative of brain hemorrhage. By extracting hierarchical features at various levels of complexity, the network achieves a comprehensive understanding of both global and fine-grained details crucial for accurate detection. Additionally, we introduce a novel training strategy to optimize the network's capacity to recognize hemorrhagic patterns while minimizing false positives.

Evaluation on a comprehensive brain image dataset demonstrates Hemo DetectNet's superior performance compared to traditional methods. The model exhibits enhanced sensitivity and specificity, effectively detecting various types of brain hemorrhages. Furthermore, our approach prioritizes interpretability, providing insights into the regions and features contributing to decision-making.

To ensure practical applicability, we explore real-time implementation considerations and computational efficiency, making Hemo DetectNet suitable for deployment in clinical settings. The combination of multi-order neural networks and advanced training strategies presented in this study represents a significant advancement in accurate and reliable brain hemorrhage detection. This research contributes to improved patient outcomes by facilitating timely medical interventions.

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Published

24.03.2024

How to Cite

Ubale, S. A. ., Gavali, A. B. ., Kakad, S. ., Jadhav, D. ., & Patil, C. D. . (2024). Brain Hemorrhage Detection Approach with Multi-Order Neural Networks. International Journal of Intelligent Systems and Applications in Engineering, 12(19s), 87–92. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5047

Issue

Vol. 12 No. 19s (2024)

Section

Research Article

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