{ "abstract": "Osteoporosis is a progressive skeletal disorder characterized by reduced bone mineral density and increased susceptibility to fractures, making early diagnosis essential for effective clinical intervention and long-term patient management. Recent advances in artificial intelligence have demonstrated significant potential for automated bone disease assessment using medical imaging modalities. In this study, a unified deep learning framework, termed OsteoNet-X, is proposed for automated osteoporosis detection and multi-class bone health classification using knee X-ray images. Unlike conventional image classification approaches, OsteoNet-X integrates YOLO-based Region of Interest (ROI) localization, DenseNet-based feature extraction, Feature-Aware Learning (FAL) optimization, and Grad-CAM-based Explainable Artificial Intelligence (XAI) to enhance both classification performance and model interpretability. The ROI extraction stage automatically identifies anatomically relevant knee joint regions, reducing background interference and enabling more discriminative feature learning. Subsequently, DenseNet facilitates efficient feature propagation and hierarchical representation learning for distinguishing normal, osteopenia, and osteoporosis conditions. The incorporation of XAI further provides visual explanations of model decisions by highlighting clinically relevant regions within the knee joint. Experimental evaluation demonstrates that OsteoNet-X achieves a classification accuracy of 97.08%, a macro F1-score of 96.61%, and a weighted F1-score of 97.11%.", "abstract_shown": true, "affiliations": [ "School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India" ], "article_type": "Research Article", "arxiv": null, "author_total": 2, "authors": [ { "affs": [ 0 ], "name": "Sudhir Kumar Sangula", "orcid": "0009-0005-4928-2760" }, { "affs": [ 0 ], "name": "S. P. Siddique Ibrahim", "orcid": "0000-0002-2189-3616" } ], "bibtex": "https://research.draconitic.com/paper/4z8ck3v8cs90v.bib", "cited_by": 0, "doi": "10.1371/journal.pone.0361033", "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13712691/", "id": "4z8ck3v8cs90v", "issns": [ "1932-6203" ], "landing_url": "https://doi.org/10.1371/journal.pone.0361033", "language": "en", "licence": "CC-BY-4.0", "licence_label": "CC BY 4.0", "licence_source": "article", "licence_url": "https://creativecommons.org/licenses/by/4.0/", "metadata_licence": null, "native_subjects": [ [ "heading", "Anatomy" ], [ "heading", "Artificial Intelligence" ], [ "heading", "Biological Tissue" ], [ "heading", "Biology and Life Sciences" ], [ "heading", "Body Limbs" ], [ "heading", "Bone" ], [ "heading", "Bone Density" ], [ "heading", "Bone Imaging" ], [ "heading", "Computer and Information Sciences" ], [ "heading", "Connective Tissue" ], [ "heading", "Connective Tissue Diseases" ], [ "heading", "Deep Learning" ], [ "heading", "Diagnostic Medicine" ], [ "heading", "Diagnostic Radiology" ], [ "heading", "Imaging Techniques" ], [ "heading", "Knee Joints" ], [ "heading", "Knees" ], [ "heading", "Legs" ], [ "heading", "Machine Learning" ], [ "heading", "Medicine and Health Sciences" ], [ "heading", "Musculoskeletal System" ], [ "heading", "Osteopenia and Osteoporosis" ], [ "heading", "Osteoporosis" ], [ "heading", "Radiology and Imaging" ], [ "heading", "Research Article" ], [ "heading", "Research and Analysis Methods" ], [ "heading", "Rheumatology" ], [ "heading", "Skeletal Joints" ], [ "heading", "Skeleton" ], [ "heading", "Women's Health" ], [ "heading", "X-Ray Radiography" ], [ "openalex", "2204|Medical Imaging and Analysis" ], [ "plos", "/Biology and life sciences/Anatomy/Biological tissue/Connective tissue/Bone/Bone density" ], [ "plos", "/Biology and life sciences/Anatomy/Body limbs/Legs/Knees" ], [ "plos", "/Biology and life sciences/Anatomy/Body limbs/Legs/Knees/Knee joints" ], [ "plos", "/Biology and life sciences/Anatomy/Musculoskeletal system/Skeleton/Bone/Bone density" ], [ "plos", "/Biology and life sciences/Anatomy/Musculoskeletal system/Skeleton/Skeletal joints/Knees" ], [ "plos", "/Biology and life sciences/Anatomy/Musculoskeletal system/Skeleton/Skeletal joints/Knees/Knee joints" ], [ "plos", "/Computer and information sciences/Artificial intelligence/Machine learning/Deep learning" ], [ "plos", "/Medicine and health sciences/Anatomy/Biological tissue/Connective tissue/Bone/Bone density" ], [ "plos", "/Medicine and health sciences/Anatomy/Body limbs/Legs/Knees" ], [ "plos", "/Medicine and health sciences/Anatomy/Body limbs/Legs/Knees/Knee joints" ], [ "plos", "/Medicine and health sciences/Anatomy/Musculoskeletal system/Skeleton/Bone/Bone density" ], [ "plos", "/Medicine and health sciences/Anatomy/Musculoskeletal system/Skeleton/Skeletal joints/Knees" ], [ "plos", "/Medicine and health sciences/Anatomy/Musculoskeletal system/Skeleton/Skeletal joints/Knees/Knee joints" ], [ "plos", "/Medicine and health sciences/Diagnostic medicine/Diagnostic radiology/Bone imaging" ], [ "plos", "/Medicine and health sciences/Diagnostic medicine/Diagnostic radiology/Bone imaging/X-ray radiography" ], [ "plos", "/Medicine and health sciences/Diagnostic medicine/Diagnostic radiology/X-ray radiography" ], [ "plos", "/Medicine and health sciences/Radiology and imaging/Diagnostic radiology/Bone imaging" ], [ "plos", "/Medicine and health sciences/Radiology and imaging/Diagnostic radiology/Bone imaging/X-ray radiography" ], [ "plos", "/Medicine and health sciences/Radiology and imaging/Diagnostic radiology/X-ray radiography" ], [ "plos", "/Medicine and health sciences/Rheumatology/Connective tissue diseases/Osteoporosis" ], [ "plos", "/Medicine and health sciences/Women's health/Osteopenia and osteoporosis" ], [ "plos", "/Research and analysis methods/Imaging techniques/Diagnostic radiology/Bone imaging" ], [ "plos", "/Research and analysis methods/Imaging techniques/Diagnostic radiology/Bone imaging/X-ray radiography" ], [ "plos", "/Research and analysis methods/Imaging techniques/Diagnostic radiology/X-ray radiography" ] ], "openalex": "W7223828884", "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0361033&type=printable", "pmcid": "PMC13712691", "pmid": "42945347", "published": "2026-10-27", "publisher": "Public Library of Science", "ris": "https://research.draconitic.com/paper/4z8ck3v8cs90v.ris", "sources": [ { "first_seen": "2026-10-30T12:17:17Z", "native_id": "10.1371/journal.pone.0361033", "source": "plos", "url": "https://doi.org/10.1371/journal.pone.0361033" }, { "first_seen": "2026-10-31T12:23:13Z", "native_id": "PMC13712691", "source": "pmc", "url": "https://doi.org/10.1371/journal.pone.0361033" } ], "subjects": [ { "field": "biology", "origin": "heading:Biology and Life Sciences", "subfield": "general" }, { "field": "medicine", "origin": "plos:diagnostic radiology", "subfield": "imaging" }, { "field": "medicine", "origin": "plos:anatomy", "subfield": "physiology" }, { "field": "medicine", "origin": "heading:Women's Health", "subfield": "reproductive-child" } ], "title": "OsteoNet-X: A deep residual learning framework for automated osteoporosis detection from knee X-ray images", "title_text": "OsteoNet-X: A deep residual learning framework for automated osteoporosis detection from knee X-ray images", "type": "peer-reviewed", "updated_at": "2026-10-31T12:23:13Z", "url": "https://research.draconitic.com/paper/4z8ck3v8cs90v", "venue": "PLOS One", "versions": [] }