Osteoporosis Detection Based on X-Ray Using Deep Convolutional Neural Network

In this study, we describe a computer-based technique for identifying osteoporosis by analyzing medical X-ray images utilizing deep convolutional neural networks (DCNNs). During the preprocessing phase, the suggested system prepares the original picture by acquiring the area of interest, enhancing contrast, and reducing noise. Subsequently, the smudging procedure was used to improve the system's accuracy and decrease mistake by creating a nearly identical fragile region throughout the database photos. The next step is using the suggested DCNN model to diagnose the problem. To do this, the dataset was preprocessed, smudged, and then put into the model in two parts: 75% for training and 25% for testing. With Dataset 1 and Dataset 2, the diagnoses' accuracy was 94.7% and 91.5, respectively. It is important to note that two datasets were used: Dataset 1 is the Osteoporosis Knee X-ray Dataset from Kaggle, which has two classes (osteopenia and osteoporosis), and Dataset 2 is from Mendeley, which contains three classes (osteopenia, normal, and osteoporosis).