A multi-agent continual learning framework for skin cancer detection leveraging crowdsourced dermoscopic images

Skin cancer represents one of the most common malignancies globally, making early detection crucial for effective treatment and improved patient outcomes. While dermatologists typically rely on dermoscopy and clinical examinations for diagnosis, recent advances in artificial intelligence, specifically deep learning techniques like convolutional neural networks (CNNs), have shown significant promise in automating skin lesion classification [1,2]. Although CNN models trained on benchmark dermatological datasets such as HAM10000 and ISIC have demonstrated diagnostic accuracies comparable to expert dermatologists, their effectiveness declines when faced with evolving real-world data distributions, a phenomenon known as concept drift [3,4].
To address the limitations associated with static AI models, this paper proposes a novel multi-agent deep learning framework designed for continual learning and adaptive skin lesion diagnosis. The architecture begins with multiple agents trained on trusted expert-annotated datasets, each subsequently specialized by continuous fine-tuning using distinct streams of dermatological images sourced from teledermatology platforms and social media. These crowd-sourced datasets capture emerging dermatological conditions, varied imaging technologies, and diverse patient demographics, providing valuable but noisy real-world data.
Crucially, the system includes a centralized Supervisor Agent responsible for periodically evaluating the performance of each specialized agent. Once annotated, these validated cases enrich the training datasets, enabling agents to continually adapt to new clinical trends and maintain robust diagnostic accuracy over time. The proposed multi-agent architecture thus integrates continual learning, domain adaptation, and expert oversight, effectively addressing concept drift and advancing practical, scalable AI-driven diagnostic support in dermatology.