A Comparative Analysis of Advanced Convolutional Neural Network Architectures for Osteoporosis Detection from Medical Images
DOI:
https://doi.org/10.15662/IJEETR.2026.0802021Keywords:
Osteoporosis, Deep Learning, Medical Imaging, Convolutional Neural Networks, ResNet18Abstract
Osteoporosis is a common skeletal condition that reduces bone strength and increases the risk of fractures, especially among women above the age of forty and older adults. Early detection plays an important role in preventing severe complications, yet access to standard diagnostic methods is often limited in many regions. With the growing use of medical imaging and artificial intelligence, deep learning models have shown promising results in automated disease classification. This paper presents a comparative study of four advanced convolutional neural network models for multi class osteoporosis detection using bone X ray and dexa images. The dataset consists of 1186 images categorized into Normal, Osteopenia, and Osteoporosis classes. VGG16, DenseNet121, InceptionV3, and ResNet18 were trained and evaluated under the same experimental conditions using transfer learning. Performance was measured using accuracy, precision, recall, and F1 score. Among the evaluated models, ResNet18 achieved the best overall performance with an accuracy of 90 percent. The results indicate that residual network architectures provide reliable feature representation while maintaining computational efficiency, making them suitable for practical osteoporosis screening applications
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