Osteoporosis is a disease in which bones weaken and become brittle as a result of decreased bone density. In osteoporosis, the bone’s resistance to impacts decreases. So, fractures can occur from simple falls and bumps. Although osteoporosis affects all bones in the body, it most commonly affects the knee, hip, vertebrae, and wrist bones. The difficult diagnosis and high treatment costs for osteoporosis increase the importance of diagnosing osteoporosis at an early stage and direct researchers to this field. In this chapter, the effectiveness of deep learning methods on X-ray images to predict knee osteoporosis is investigated. Transfer learning of convolutional neural networks such as VGG-16, DenseNet169, Xception, InceptionV3, ResNet50V2, and InceptionResNetV2 was used to classify X-ray images of knee joints as normal, osteopenia, and osteoporosis. The experiments showed that the DenseNet169 model outperformed other deep learning models with an F1 score of 92.72% and AUC score of 0.97%. It is concluded that the experimental results strengthen the hypothesis that transfer learning-based deep learning models can be used as intelligent medical assistants for early detection of osteoporosis.