Mandal Sayantani, Ghosh Subhayu, Jana Nanda Dulal, Chakraborty Somenath, Mallik Saurav
Department of Mathematics, National Institute of Technology Durgapur, West Bengal, India.
Department of Computer Science and Engineering, National Institute of Technology Durgapur, West Bengal, India.
J Imaging Inform Med. 2024 Nov 18. doi: 10.1007/s10278-024-01327-z.
Skin cancer is a critical global health issue, with millions of non-melanoma and melanoma cases diagnosed annually. Early detection is essential to improving patient outcomes, yet traditional deep learning models for skin cancer classification are often limited by the need for large, annotated datasets and extensive computational resources. The aim of this study is to address these limitations by proposing an efficient skin cancer classification framework that integrates active learning (AL) with particle swarm optimization (PSO). The AL framework selectively identifies the most informative unlabeled instances for expert annotation, minimizing labeling costs while optimizing classifier performance. PSO, a nature-inspired metaheuristic algorithm, enhances the selection process within AL, ensuring the most relevant data points are chosen. This method was applied to train multiple Convolutional Neural Network (CNN) models on the HAM10000 skin lesion dataset. Experimental results demonstrate that the proposed AL-PSO approach significantly improves classification accuracy, with the Least Confidence strategy achieving approximately 89.4% accuracy while using only 40% of the labeled training data. This represents a substantial improvement over traditional approaches in terms of both accuracy and efficiency. The findings indicate that the integration of AL and PSO can accelerate the adoption of AI in clinical settings for skin cancer detection. The code for this study is publicly available at ( https://github.com/Sayantani-31/AL-PSO ).
皮肤癌是一个关键的全球健康问题，每年有数百万例非黑色素瘤和黑色素瘤病例被诊断出来。早期检测对于改善患者预后至关重要，然而用于皮肤癌分类的传统深度学习模型通常受到对大型标注数据集和大量计算资源需求的限制。本研究的目的是通过提出一种将主动学习（AL）与粒子群优化（PSO）相结合的高效皮肤癌分类框架来解决这些限制。主动学习框架选择性地识别最具信息性的未标记实例以供专家标注，在优化分类器性能的同时最小化标注成本。粒子群优化算法是一种受自然启发的元启发式算法，它增强了主动学习中的选择过程，确保选择最相关的数据点。该方法应用于在HAM10000皮肤病变数据集上训练多个卷积神经网络（CNN）模型。实验结果表明，所提出的主动学习 - 粒子群优化方法显著提高了分类准确率，采用最小置信度策略时，仅使用40%的标记训练数据就能达到约89.4%的准确率。这在准确性和效率方面都比传统方法有了显著提高。研究结果表明，主动学习和粒子群优化的整合可以加速人工智能在皮肤癌检测临床环境中的应用。本研究的代码可在（https://github.com/Sayantani - 31/AL - PSO）上公开获取。
