Tbaileh, ImadBağrıyanık, Selami2025-11-042025-11-042025Tbaileh, I., & Bağrıyanık, S. (2025). Visual quality assessment of E-commerce product images using convolutional neural networks. Multimedia Systems, 31(1), pp. 1-14. https://doi.org/10.1007/s00530-025-02009-81432-18820942-4962https://doi.org/10.1007/s00530-025-02009-8https://hdl.handle.net/20.500.13055/1159High-quality product images are vital in shaping consumer trust and driving engagement on e-commerce platforms. This study proposes a deep learning-based approach for evaluating the visual quality of product images, with the aim of improving the overall customer experience and presentation standards in online marketplaces. A custom-labeled dataset was developed, containing thousands of product images categorized into five quality levels. A convolutional neural net work (CNN) was trained to classify these images based on their visual quality. In addition, two well-known architectures, MobileNetV2 and EfficientNetB0, were trained under identical conditions to serve as benchmarks for performance com parison. The proposed CNN model achieved an accuracy of 94.93%, outperforming both MobileNetV2 (76.60%) and EfficientNetB0 (92.77%). It also delivered the highest performance in terms of precision, recall, and F1-score, confirming its effectiveness in this domain. The results highlight the CNN model’s suitability for real-time quality assessment of e-commerce images. Its strong performance and efficiency make it a promising candidate for integration into commercial platforms. Future work will investigate the use of transformer-based models and more diverse training data to further improve accuracy and generalizability.eninfo:eu-repo/semantics/closedAccessComputer VisionNR-IQACNNDeep LearningImage Quality AssessmentE-CommerceArticle10.1007/s00530-025-02009-8311114Q22-s2.0-105018672726Q1