Enhancing Dermatological Diagnosis Through Medical Image Analysis: How Effective Is YOLO11 Compared to Leading CNN Models?

Abstract

Skin diseases represent a major worldwide health hazard affecting millions of people yearly and substantially compromising healthcare systems. Particularly in areas where dermatologists are scarce, standard diagnostic techniques, which mostly rely on visual inspection and clinical experience, are frequently subjective, time-consuming, and prone to mistakes. This investigation undertakes a comparative analysis of four state-of-the-art deep learning architectures, YOLO11, YOLOv8, VGG16, and ResNet50, in the context of skin disease identification. This study evaluates the performance of these models using pivotal metrics, building upon the foundation of the YOLO paradigm, which revolutionized spatial attention and multi-scale representation. A properly selected collection of 900 high-quality dermatological images with nine disease categories was used for investigation. Robustness and generalizability were guaranteed by using data augmentation and hyperparameter adjustment. By varying benchmark models in balancing accuracy and recall while limiting false positives and false negatives, YOLO11 obtained a test accuracy of 80.72%, precision of 88.7%, recall of 86.7%, and an F1 score of 87.0%. The expedition performance of YOLO11 signifies a promising trajectory in the development of highly accurate skin disease detection models. Our analysis not only highlights the strengths and weaknesses of the model but also underscores the rapid development of deep learning techniques in medical imaging.

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