IoT-Driven Skin Cancer Detection: Active Learning and Hyperparameter Optimization for Enhanced Accuracy

Skin cancer, one of the most prevalent and lethal cancer types, poses significant challenges for early diagnosis due to the diversity in lesion size, shape, color, and surface reflections. The Internet of Things (IoT) has revolutionized healthcare by enabling real-time data exchange and supporting advancements in automated diagnosis through deep learning (DL) techniques such as convolutional neural networks (CNNs). However, CNNs often require large, labeled datasets, which are costly and time-consuming to compile. To address these challenges, we propose an innovative active learning (AL) framework driven by deep reinforcement learning (DRL) and a novel scope loss function. This framework optimizes classification while reducing reliance on extensive labeled data. Unlike traditional active learning techniques that rely on static selection methods, our model dynamically incorporates deep reinforcement learning (DRL) for strategic sample selection during training. The scope loss function balances the exploitation of labeled data with the exploration of new, unlabeled data, enabling efficient training. Additionally, an enhanced artificial bee colony (ABC) algorithm with a mutual learning strategy optimizes hyperparameter tuning, boosting model performance. Evaluated on the International Skin Imaging Collaboration (ISIC) and human against machines 10000 images (HAM10000) datasets, the proposed framework achieved high accuracy, with F-measures of 92.791% and 91.984%, respectively. This novel approach demonstrates significant potential to advance early skin cancer detection, offering a reliable and efficient tool for healthcare professionals.