###### Abstract  
Due to the physical strain experienced during intense workouts, athletes are at a heightened risk of developing osteopenia and osteoporosis. These conditions not only impact their overall health but also their athletic performance. The current clinical screening methods for osteoporosis are limited by their high radiation dose, complex post-processing requirements, and the significant time and resources needed for implementation. This makes it challenging to incorporate them into athletes' daily training routines. Consequently, our objective was to develop an innovative automated screening approach for detecting osteopenia and osteoporosis using X-ray image data. Although several automated screening methods based on deep learning have achieved notable results, they often suffer from overfitting and inadequate datasets. To address these limitations, we proposed a novel model called the GLCM-based fuzzy broad learning system (GLCM-based FBLS). Initially, texture features of X-ray images were extracted using the gray-level co-occurrence matrix (GLCM). Subsequently, these features were combined with the fuzzy broad learning system to extract crucial information and enhance the accuracy of predicting osteoporoptic conditions. Finally, we applied the proposed method to the field of osteopenia and osteoporosis screening. By comparing this model with three advanced deep learning models, we have verified the effectiveness of GLCM-based FBLS in the automatic screening of osteoporosis for athletes. s +
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these methods are effective and interpretable, their learning ability is not robust enough for large-scale datasets, raising questions about their performance on multi-center, large-scale datasets. Deep learning, a popular machine learning technology, has emerged as a promising approach for osteopenia and osteoporosis screening [7]. By leveraging deep learning techniques, researchers have proposed methods that can automatically extract features from imaging data and perform screening for osteopenia and osteoporosis [8; 9; 10]. These deep learning methods demonstrate excellent classification performance and have made significant advancements in detecting osteoporosis based on radiographs in various regions, such as the chest and lumbar spine. However, these methods often require a substantial amount of data, and overfitting can occur when the sample size is small. Additionally, the complex parameter settings and network structures of deep learning systems pose challenges in model training [11]. The broad learning system (BLS) is a flat network that has a simpler structure and fewer parameters compared to deep learning [12]. It can quickly extract important features from raw data using sparse auto-encoders and random matrices. This makes it a potential alternative for deep learning in automated osteoporosis screening. However, when trained on X-ray image datasets, the BLS may struggle to obtain effective information and achieve satisfactory results due to the complexity of the information contained within the images. The fuzzy broad learning system (FBLS) that combines fuzzy logic with the BLS is capable for mining critical information from input data that may contain uncertainty or imprecision. This makes the FBLS suitable for real-world problems where the data may be vague or uncertain. Inspired by the robust learning ability of the FBLS, we propose using the FBLS for osteopenia and osteoporosis screening to improve performance. However, directly applying the FBLS to process X-ray images may still limit the screening performance due to the presence of redundant information and noise in the images. Feature extraction is a process in which pertinent information or features are obtained from raw data. A proficient feature extractor has the ability to acquire critical features while eliminating redundant information from the original data [13]. In [14], [PERSON] and [PERSON] explored the application of the gray-level co-occurrence matrix (GLCM) for extracting texture features from medical images.