Haoshen Qin’s research while affiliated with University of Florida and other places

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.

Text summarization is crucial in various sectors, such as engineering and healthcare, because it enhances efficiency in terms of time and costs. Current extractive text summarization methods struggle with challenges such as greedy selection, model generalization limitations, and high computational demands. To solve these problems, this research introduces a novel extractive text summarization method that uniquely integrates a Generative Adversarial Network (GAN), Transductive Long Short-Term Memory (TLSTM), and DistilBERT for sentence embedding. Our technique uses GANs, which include generator and discriminator components, with the core design based on TLSTM. TLSTM utilizes transductive learning to improve accuracy by focusing on samples geographically closer to the test data. In our model, the generator considers whether to include a sentence in the summary while the discriminator critically reviews the generated summary. This GAN model reduces greedy sentence selection, enhancing summary coherence and quality. We implement a Reinforcement Learning (RL)-based strategy to address an imbalance caused by more fake than real samples in the discriminator. This RL approach, novel in the context of GANs for summarization, views training as a sequence of interconnected decisions, treating each sample as a unique scenario. The network, acting as the decision-making agent, assigns greater rewards or penalties to the minority class to correct the imbalance. The effectiveness of our model was evaluated using the well-regarded CNN/Daily Mail dataset, achieving ROUGE-1, ROUGE-2, and ROUGE-L scores of 52.45, 26.46, and 44.85, respectively. Compared to existing methods, our results demonstrate a significant improvement in summarization quality and operational efficiency, as measured by the ROUGE metric.