eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2025-02-20 18 March Spl Edition 271 282 10.13005/bpj/3087 63873 article Geetha Ramamoorthy 1 Arulselvi Subramanian 1 Tamilselvi Rajendran 2 Parisa Beham Mohamed 2 Department of ECE, Bharath Institute of Higher Education and research, Chennai, India Department of ECE, Sethu Institute of Technology, Viruthunagar, India Elevated noise levels and low resolution often obscure critical bone structures in DXA and X-ray images; this complication can hinder the diagnosis of osteoporosis. This paper presents a comprehensive comparative analysis of four distinct image processing methods: anisotropic diffusion, multi-scale wavelet analysis, adaptive guided filtering and neural network-based denoising. Each technique is evaluated for its effectiveness in reducing noise, preserving image details and enhancing diagnostic accuracy. Anisotropic diffusion effectively reduces noise while maintaining edge clarity—this ensures that vital bone structures remain visible. Multi-scale wavelet analysis captures intricate details over various scales, providing a robust approach to highlight essential regions. Adaptive guided filtering sharpens edges and improves precision by minimizing distortions. However, the neural network-based denoising method stands out among these techniques, significantly outperforming the others. This advanced deep learning filter not only eliminates residual noise but also protects critical bone structures, thus ensuring remarkable diagnostic clarity. Quantitative and qualitative analyses, utilizing metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), confirm that the neural network-based denoising approach achieves an optimal balance between noise reduction and structural preservation. Building on these findings, we propose a novel enhancement to the neural network-based denoising method: this improvement makes it more effective for clinical applications. This work provides valuable insights for clinical use, enabling more sensitive and accurate early diagnosis of osteoporosis. Although the study demonstrates the potential of advanced image processing techniques, it also highlights the need for further research to fully realize their benefits. This offers a significant contribution to medical imaging practices; however, challenges remain in implementation. But, the implications of these findings are profound, particularly because they can reshape the future of diagnostic methodologies. https://biomedpharmajournal.org/vol18marchspledition/comparative-analysis-of-image-denoising-techniques-for-osteoporosis-detection-using-dxa-and-x-ray-imaging/ Image Preprocessing Medical Imaging Enhancement Neural Network Denoising Osteoporosis Diagnosis