Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Transfer Learning-Based Osteoporosis Classification Using Simple Radiographs Subject The topic of the resource BMD; CNN models; DXA; X-rays Description An account of the resource Osteoporosis is a condition that affects the entire skeletal system, resulting in a decreased density of bone mass and the weakening of bone tissues micro-architecture. This leads to weaker bones that are more susceptible to fractures. Detecting and measuring bone mineral density has always been a critical area of focus for researchers in the diagnosis of bone diseases such as osteoporosis. However, existing algorithms used for osteoporosis diagnosis encounter challenges in obtaining accurate results due to X-ray image noise and variations in bone shapes, especially in low-contrast conditions. Therefore, the development of efficient algorithms that can mitigate these challenges and improve the accuracy of osteoporosis diagnosis is essential. In this research paper, a comparative analysis was conducted Assessing the accuracy and efficiency of the latest deep learning CNN model, such as VGG16, VGG19, DenseNet121, Resnet50, and InceptionV3 in detecting to Classify Normal and Osteoporosis cases. The study employed 830 X-ray images of the Spine, Hand, Leg, Knee, and Hip, comprising Normal (420) and Osteoporosis (410) cases. Various performance metrics were utilized to evaluate each model. The findings indicate that DenseNet121 exhibited superior performance with an accuracy rate of 93.4% Achieving an error rate of 0.07 and a validation loss of only 0.57 compared with other models considered in this study. 2023, International journal of online and biomedical engineering. All Rights Reserved. Creator An entity primarily responsible for making the resource Dodamani P.S.; Danti A. Source A related resource from which the described resource is derived International journal of online and biomedical engineering, Vol-19, No. 8, pp. 66-87. Publisher An entity responsible for making the resource available International Association of Online Engineering Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.3991/ijoe.v19i08.39235" target="_blank" rel="noreferrer noopener">https://doi.org/10.3991/ijoe.v19i08.39235</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164622680&amp;doi=10.3991%2Fijoe.v19i08.39235&amp;partnerID=40&amp;md5=b3a7889c49ececa313337be7417578f5" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164622680&amp;doi=10.3991%2fijoe.v19i08.39235&amp;partnerID=40&amp;md5=b3a7889c49ececa313337be7417578f5</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 26268493 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Dodamani P.S., Christ University, Karnataka, Bangalore, India; Danti A., Christ University, Karnataka, Bangalore, India