Hallux valgus is a common foot deformity. Traditional diagnosis mainly relies on X-ray images, which present radiation risks and require professional equipment, limiting their use in daily screening. In addition, in large-scale community screenings and resource-limited regions, where rapid processing of numerous patients is required, access to radiographic equipment or specialists may be constrained. Therefore, this study improves the MobileNetV3 model to automatically determine the presence of hallux valgus from digital foot photographs. In this study, we used 2934 foot photos from different organizations, combined with the segment anything model (SAM) to extract foot regions and replace the photo backgrounds to simulate different shooting scenarios, and used data enhancement techniques such as rotations and noise to extend the training set to more than 10,000 images to improve the diversity of the data and the model's generalization ability. We evaluated several classification models and achieved over 95% accuracy, precision, recall, and F1 score by training the improved MobileNetV3. Our model offers a cost-effective, radiation-free solution to reduce clinical workload and enhance early diagnosis rates in underserved areas.