eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2025-09-30 18 3 1975 1990 10.13005/bpj/3230 67392 article Tamilselvi Rajendran 1 Parisa Beham Mohamed 1 Sathiya Pandiya Lakshmi 1 Nandhineeswari 1 Shanmuga Priya 1 Department of ECE, Sethu Institute of Technology, Viruthunagar, India Osteoporosis is a progressive skeletal disorder characterized by reduced bone density and increased fracture risk, particularly in older populations. Traditional diagnostic methods like DXA scans, while accurate, are costly, involve radiation exposure, and lack accessibility in low-resource environments. This study proposes a novel, non-invasive diagnostic pipeline for osteoporosis detection using simulated ultrasound signals. The approach incorporates advanced signal preprocessing (Fourier Transform and Wavelet Decomposition) and a custom 1D-Convolutional Neural Network (1D-CNN) tailored for sequential time-series data. The model achieved an accuracy of 95.6%, sensitivity of 94.8%, and precision of 96.2%, outperforming traditional classifiers such as Random Forest and Support Vector Machine (SVM). The integration of portable ultrasound and deep learning presents a promising solution for real-time, accessible osteoporosis screening in underserved clinical settings. The study is based on simulated ultrasound data, which emulates realistic bone tissue responses, and lays the foundation for future validation with real-world datasets. https://biomedpharmajournal.org/vol18no3/ultrasound-based-non-invasive-osteoporosis-detection-using-advanced-deep-learning-techniques/ Bone Density Convolutional Neural Network Fourier Transform Osteoporosis Detection Quantitative Ultrasound Wavelet Decomposition