of Plastic Surgeons AS. Plastic Surgery Statistics Report 2020. American Society of Plastic Surgeons; 2021. Available from: https://www.plasticsurgery.org/documents/news/statistics/2020/plastic-surgery-statistics-full-report-2020.pdf.

Yi KH, Lee JH, Hu HW, Kim HJ. Novel anatomical guidelines on botulinum neurotoxin injection for wrinkles in the nose region. Toxins. 2022;14(5):342.

Ekman P, Friesen WV. Facial Action Coding System. Environmental Psychology & Nonverbal Behavior. 1978;.

Redmon J, Divvala S, Girshick R, Farhadi A. You only look once: Unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. p. 779–788.

Redmon J, Farhadi A. YOLO9000: Better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2017. p. 7263–7271.

Redmon J, Farhadi A. YOLOv3: An incremental improvement. arXiv preprint arXiv:1804.02767. 2018;.

Bochkovskiy A, Wang CY, Liao HYM. YOLOv4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934. 2020;.

Jocher G.: Ultralytics YOLOv5. Version 7.0, AGPL-3.0. https://github.com/ultralytics/yolov5.

Li C, Li L, Jiang H, Weng K, Geng Y, et al. YOLOv6: A single-stage object detection framework for industrial applications. arXiv preprint arXiv:2209.02976. 2022;.

Wang CY, Bochkovskiy A, Liao HYM. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition; 2023. p. 7464–7475.

Liu Q, Ye H, Wang S, Xu Z. YOLOv8-CB: dense pedestrian detection algorithm based on in-vehicle camera. Electronics. 2024;13(1):236.

Sahin O, Ozer S. YOLODrone+: Improved YOLO architecture for object detection in UAV images. In: 2022 30th Signal Processing and Communications Applications Conference (SIU). IEEE; 2022. p. 1–4.

Ngo LH, Luong M, Sirakov NM, Viennet E, Le-Tien T. Skin lesion image classification using sparse representation in quaternion wavelet domain. Signal, Image and Video Processing. 2022;16(7):1721–1729.

Wei Z, Li Q, Song H. Dual attention based network for skin lesion classification with auxiliary learning. Biomedical Signal Processing and Control. 2022;74:103549.

Yue G, Wei P, Zhou T, Jiang Q, Yan W, Wang T. Toward multicenter skin lesion classification using deep neural network with adaptively weighted balance loss. IEEE Transactions on Medical Imaging. 2022;42(1):119–131.

Hoang L, Lee SH, Lee EJ, Kwon KR. Multiclass skin lesion classification using a novel lightweight deep learning framework for smart healthcare. Applied Sciences. 2022;12(5):2677.

Zhuang D, Chen K, Chang JM. CS-AF: A cost-sensitive multi-classifier active fusion framework for skin lesion classification. Neurocomputing. 2022;491:206–216.

Hsu BWY, Tseng VS. Hierarchy-aware contrastive learning with late fusion for skin lesion classification. Computer methods and programs in biomedicine. 2022;216:106666.

Ayas S. Multiclass skin lesion classification in dermoscopic images using swin transformer model. Neural Computing and Applications. 2023;35(9):6713–6722.

Baykal Kablan E, Ayas S. Skin lesion classification from dermoscopy images using ensemble learning of ConvNeXt models. Signal, Image and Video Processing. 2024;18(8):6353–6361.

Aruk I, Pacal I, Toprak AN. A novel hybrid ConvNeXt-based approach for enhanced skin lesion classification. Expert Systems with Applications. 2025;p. 127721.

Deepa H, Gowrishankar S, Veena A. A deep learning-based detection of wrinkles on skin. In: Computational Vision and Bio-Inspired Computing: Proceedings of ICCVBIC 2021. Cham, Switzerland: Springer; 2022. p. 25–37.

Kim S, Yoon H, Lee J, Yoo S. Semi-automatic labeling and training strategy for deep learning-based facial wrinkle detection. In: 2022 IEEE 35th international symposium on computer-based medical systems (CBMS). IEEE; 2022. p. 383–388.

Ismail MIM, Sung AN. Acne lesion and wrinkle detection using faster R-CNN with ResNet-50. In: AIP Conference Proceedings. vol. 2676. AIP Publishing LLC; 2022. p. 020007.

Musafer H, Tokgöz E. A facial wrinkle detection by using deep learning with an efficient optimizer. In: Cosmetic and reconstructive facial plastic surgery: A review of medical and biomedical engineering and science concepts; 2023. p. 381–388.

Moon J, Chung H, Jang I. Facial wrinkle segmentation for cosmetic dermatology: Pretraining with texture map-based weak supervision. In: International Conference on Pattern Recognition. Springer; 2024. p. 319–334.

Yadav N, Alfayeed SM, Khamparia A, Pandey B, Thanh DN, Pande S. HSV model-based segmentation driven facial acne detection using deep learning. Expert Systems. 2022;39(3):e12760.

Khalid U, Li C, Khan AA, Mehmood F. AC-Skin: Facial Acne Detection-Based on Intelligent Learning and Large Data Collection of Internet of Things (IoT) for Smart Skincare. IEEE Sensors Journal. 2024;24(19):30769–30777.

Woo S, Park J, Lee JY, Kweon IS. Cbam: Convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV); 2018. p. 3–19.

Tan M, Pang R, Le QV. Efficientdet: Scalable and efficient object detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition; 2020. p. 10781–10790.

Jocher G, Chaurasia A, Qiu J.: Ultralytics YOLOv8. Version 8.0.0, AGPL-3.0. https://github.com/ultralytics/ultralytics.

Gulsoy T, Kablan EB, Ayas S, Selcuk LB, Arica DA. Automatic Botox Injection Points Detection Based on Deep Learning. In: 2025 7th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA). IEEE; 2025. p. 1–8.

Xu C, Dong Z, Zhong S, Chen Y, Pan S, Wu M. Fusion network for small target detection based on YOLO and attention mechanism. Optoelectronics Letters. 2024;20(6):372–378.

Wu Z, Wu D, Li N, Chen W, Yuan J, Yu X, et al. Cbgs-yolo: A lightweight network for detecting small targets in remote sensing images based on a double attention mechanism. Remote Sensing. 2024;17(1):109.

Qu S, Dang C, Chen W, Liu Y. SMA-YOLO: An Improved YOLOv8 Algorithm Based on Parameter-Free Attention Mechanism and Multi-Scale Feature Fusion for Small Object Detection in UAV Images. Remote Sensing. 2025;17(14):2421.

Tschandl P, Rosendahl C, Kittler H. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific data. 2018;5(1):1–9.

ISIC.: ISIC Challenge 2018. Accessed: YYYY-MM-DD. https://challenge2018.isic-archive.com/.

ISIC.: ISIC Challenge. Accessed: 2022-01-09. https://challenge.isic-archive.com/.

Alzahrani S, Al-Bander B, Al-Nuaimy W. Attention mechanism guided deep regression model for acne severity grading. Computers. 2022;11(3):31.

Wang J, Wang C, Wang Z, Hounye AH, Li Z, Kong M, et al. A novel automatic acne detection and severity quantification scheme using deep learning. Biomedical Signal Processing and Control. 2023;84:104803.

Kim DH, Sun S, Cho SI, Kong HJ, Lee JW, Lee JH, et al. Automated facial acne lesion detecting and counting algorithm for acne severity evaluation and its utility in assisting dermatologists. American Journal of Clinical Dermatology. 2023;24(4):649–659.

Zhang D, Jin C, Zhang Z, Cao X, Xue C. Automatic acne detection model based on improved YOLOv7. IEEE Access. 2024;.

Phillips PJ, Wechsler H, Huang J, Rauss PJ. The FERET database and evaluation procedure for face-recognition algorithms. Image and vision computing. 1998;16(5):295–306.

Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2015. p. 3431–3440.

Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. Springer; 2015. p. 234–241.

Chen LC, Zhu Y, Papandreou G, Schroff F, Adam H. Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European conference on computer vision (ECCV); 2018. p. 801–818.

Hatamizadeh A, Nath V, Tang Y, Yang D, Roth HR, Xu D. Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI brainlesion workshop. Springer; 2021. p. 272–284.

Das D, Mukherjee K. Advanced YOLOv8 with CBAM and BiFPN for Assessing Coral Health in Underwater Environments. In: 2024 IEEE Silchar Subsection Conference (SILCON 2024). IEEE; 2024. p. 1–6.

Yu H, Wang J, Han Y, Fan B, Zhang C. Research on an intelligent identification method for wind turbine blade damage based on CBAM-BiFPN-YOLOV8. Processes. 2024;12(1):205.

Zhou X, Chen W, Wei X. Improved Field Obstacle Detection Algorithm Based on YOLOv8. Agriculture. 2024;14(12):2263.

Yang Z, Li Y, Han Q, Wang H, Li C, Wu Z. A Method for Tomato Ripeness Recognition and Detection Based on an Improved YOLOv8 Model. Horticulturae. 2024;11(1):15.

Chen C, Zhou Q, Li S, Luo D, Tan G. Fabric defect detection algorithm based on improved YOLOv8. Textile Research Journal. 2025;95(3-4):235–251.

Hu Y, Zeng Q. Small Target Detection in UAV Aerial Images Based on Sliding Window and Improved Yolov8. In: 2024 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML). IEEE; 2024. p. 1161–1165.

Huang S, Shen J, Ling Z, Wang X, Zhang D, Wang J. Target Detection Algorithm Based on Improved YOLOv8 for Hynobius Amjiensis. In: 2024 5th International Conference on Computer Engineering and Application (ICCEA). IEEE; 2024. p. 1257–1262.

Lin TY, Dollar P, Girshick R, He K, Hariharan B, Belongie S. Feature Pyramid Networks for Object Detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2017. .

Liu S, Qi L, Qin H, Shi J, Jia J. Path Aggregation Network for Instance Segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2018.