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has been cited by the following article:

• TITLE: Early Detection of Sexually Transmitted Infections Using YOLO 12: A Deep Learning Approach

  AUTHORS: Martin Chileshe, Mayumbo Nyirenda, John Kaoma

  KEYWORDS: Sexually Transmitted Infections (STIs), YOLO 12, Skin Diagnosis, Computer Vision, Generative AI

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.15 No.4, April 27, 2025

  ABSTRACT: This paper focuses on the use of YOLOv12 for the early detection of Sexually Transmitted Infections, which are a global public health challenge. YOLOv12 is a deep-learning model released on February 18th, 2025. Its release has shifted from the traditional CNN-based approaches to attention-centric architecture yet still maintains high accuracy, fast inference and robust object detection capabilities with better global context modeling. This raises many interesting questions, such as whether it can perform better on real-world problems such as early detection STIs. Can the model show consistent results on different skin tones? Can it help reduce the risk of long-term effects of untreated STIs? Can YOLOv12 outperform YOLOv10 and YOLOv11? How can we validate the results? This study will answer these questions and show us how we arrived at our conclusions.