Develop A Hybrid Improved Weighed Pigeon Optimization with Faster Mask Recurrent Convolutional Neural Network to Classify and Detect Bone Fracture

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 5

Year of Publication : 2025

Authors : R. Jothi, K. Jayanthi

10.14445/23488549/IJECE-V12I5P102

How to Cite?

R. Jothi, K. Jayanthi, "Develop A Hybrid Improved Weighed Pigeon Optimization with Faster Mask Recurrent Convolutional Neural Network to Classify and Detect Bone Fracture," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 5, pp. 8-18, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I5P102

Abstract:

This research proposes a novel approach for bone fracture classification and detection utilizing a hybrid Improved Weighed Pigeon Optimization (IWPO) algorithm coupled with a Faster Mask Recurrent Convolutional Neural Network (FMRCNN). The IWPO algorithm, an improved method of the traditional Pigeon Optimization Algorithm (POA), introduces weighted factors to achieve a dynamic adjustment of the search process during operation to increase the convergence speed and accuracy of the solutions. The FMRCNN architecture, an evolution of classic CNN models, relies on recurrent links and an effective mask approach for better feature retrieval and positioning capabilities-the LOFAR observations reveal no potential specific behavior processes. IWPO and FMRCNN are hybridized to foster joint efforts of metaheuristic optimization and deep learning methods to optimize the network for bone fracture classification and detection tasks. The experimental results show that the proposed method outperforms the traditional methods in terms of accuracy, efficiency, and robustness in bone fracture diagnosis based on the medical imaging data. This work advances current methods in medical image analysis, providing a potential framework for automated fracture diagnosis and clinical decision support systems.

Keywords:

Hybrid Improved Weighed Pigeon Optimization, Faster Mask Recurrent Convolutional Neural Network, Bone fracture classification, Fracture detection, Medical imaging analysis, Metaheuristic optimization, Deep learning.

References:

[1] Muhammet Emin Sahin et al., “Image Processing and Machine Learning‐Based Bone Fracture Detection and Classification using X‐ray Images,” International Journal of Imaging Systems and Technology, vol. 33, no. 3, pp. 853-865, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Santoshachandra Rao Karanam, Y. Srinivas, and S. Chakravarty, “A Supervised Approach to Musculoskeletal Imaging Fracture Detection and Classification Using Deep Learning Algorithms,” Computer Assisted Methods in Engineering and Science, vol. 30, no. 3, pp. 369-385, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[3] K. Karthik, and S. Sowmya Kamath, “MSDNet: A Deep Neural Ensemble Model for Abnormality Detection and Classification of Plain Radiographs,” Journal of Ambient Intelligence and Humanized Computing, vol. 14, pp. 16099-16113, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Tushar Verma et al., “Bone Fracture Detection using Machine Learning,” AIP Conference Proceedings, vol. 3072, no. 1, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Vipul Narayan et al., “FuzzyNet: Medical Image Classification based on GLCM Texture Feature,” 2023 International Conference on Artificial Intelligence and Smart Communication (AISC), Greater Noida, India, pp. 769-773, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Juan Li et al., “Medical Image Identification Methods: A Review,” Computers in Biology and Medicine, vol. 169, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Manimuthu Ayyannan et al., “Medical Image Classification using Deep Learning Techniques: A Review,” 2023 Second International Conference on Electronics and Renewable Systems (ICEARS), Tuticorin, India, pp. 1327-1332, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Mathil K. Thamer, Zakariya Yahya Algamal, and Raoudha Zine, “Enhancement of Kernel Clustering Based on Pigeon Optimization Algorithm,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 31, pp. 121-133, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Swagato Das, and Purnachandra Saha, “A Novel Pigeon Nesting Algorithm Based on the Nesting Behaviour of Pigeon for Health Monitoring of Structure,” Journal of Vibration Engineering & Technologies, vol. 12, pp. 3265-3287, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Wei-Jie Lucas Chong, Siew-Chin Chong, and Thian-Song Ong, “Masked Face Recognition using Histogram-Based Recurrent Neural Network,” Journal of Imaging, vol. 9, no. 2, pp. 1-15, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Leonardo Tanzi et al., “X-Ray Bone Fracture Classification using Deep Learning: A Baseline for Designing a Reliable Approach,” Applied Sciences, vol. 10, no. 4, pp. 1-17, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Tanushree Meena, and Sudipta Roy, “Bone Fracture Detection using Deep Supervised Learning from Radiological Images: A Paradigm Shift,” Diagnostics, vol. 12, no. 10, pp. 1-17, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[13] B. Pujitha et al., “Detection of Bone Fracture Using Deep Learning,” 2024 International Conference on Emerging Systems and Intelligent Computing (ESIC), Bhubaneswar, India, pp. 703-708, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Khan M. Imran et al., “Bone Fracture Identification with Deep Learning Model using Resnet50,” International Journal of Computing and Digital Systems, vol. 16, pp. 1-14, 2024.
[Google Scholar] [Publisher Link]
[15] Komal Ghoti, Ujjwal Baid, and Sanjay Talbar, “MURA: Bone Fracture Segmentation Using a U-net Deep Learning in X-Ray Images,” Techno-Societal 2020: Proceedings of the 3rd International Conference on Advanced Technologies for Societal Applications, pp. 519-531, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Leonardo Tanzi et al., “Hierarchical Fracture Classification of Proximal Femur X-Ray Images using a Multistage Deep Learning Approach,” European Journal of Radiology, vol. 133, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Saurabh Verma et al., Detecting Bone Fracture Using Transfer Learning, Advancement of Machine Intelligence in Interactive Medical Image Analysis, pp. 215-228, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Deepa Joshi, Thipendra P. Singh, and Anil Kumar Joshi, “Deep Learning-Based Localization and Segmentation of Wrist Fractures on X-ray Radiographs,” Neural Computing and Applications, vol. 34, pp. 19061-19077, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Sabrina Chin-yun Shen et al, “Deep Learning Approach to Assess Damage Mechanics of Bone Tissue,” Journal of the Mechanical Behavior of Biomedical Materials, vol. 123, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Abhimanyu Verma et al., “Humerus Bone Fracture Detection Utilizing YOLOv4 Algorithm: A Deep Learning Approach,” 2024 2nd International Conference on Disruptive Technologies (ICDT), Greater Noida, India, pp. 1191-1196, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[21] B. Narendra Kumar Rao et al., “Bone Fracture Detection and Classification Using Deep Learning Techniques,” Driving Smart Medical Diagnosis Through AI-Powered Technologies and Applications, pp. 92-10, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Fatih Uysal et al., “Classification of Shoulder X-Ray Images with Deep Learning Ensemble Models,” Applied Sciences, vol. 11, no. 6, pp. 1-33, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Fan Xu et al., “Deep Learning-Based Artificial Intelligence Model for Classification of Vertebral Compression Fractures: A Multicenter Diagnostic Study,” Frontiers in Endocrinology, vol. 14, pp. 14-11, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Mansoor Habib Mazumder, and Maheshwari Prasad Singh, “Bone Cancer Detection Using Deep Learning,” Innovations in Computer Science and Engineering, pp. 285-296, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Soaad M. Naguib et al., “Classification of Cervical Spine Fracture and Dislocation Using Refined Pre-Trained Deep Model and Saliency Map,” Diagnostics, vol. 13, no. 7, pp. 1-16, 2023.
[CrossRef] [Google Scholar] [Publisher Link]