Department of Computer Science, Bahria University Lahore Campus, Lahore, Pakistan.
School of Biochemistry and Biotechnology, University of the Punjab, Lahore, Pakistan.
BMC Med Inform Decis Mak. 2024 Sep 27;24(1):265. doi: 10.1186/s12911-024-02686-x.
Segmentation of skin lesions remains essential in histological diagnosis and skin cancer surveillance. Recent advances in deep learning have paved the way for greater improvements in medical imaging. The Hybrid Residual Networks (ResUNet) model, supplemented with Ant Colony Optimization (ACO), represents the synergy of these improvements aimed at improving the efficiency and effectiveness of skin lesion diagnosis.
This paper seeks to evaluate the effectiveness of the Hybrid ResUNet model for skin lesion classification and assess its impact on optimizing ACO performance to bridge the gap between computational efficiency and clinical utility.
The study used a deep learning design on a complex dataset that included a variety of skin lesions. The method includes training a Hybrid ResUNet model with standard parameters and fine-tuning using ACO for hyperparameter optimization. Performance was evaluated using traditional metrics such as accuracy, dice coefficient, and Jaccard index compared with existing models such as residual network (ResNet) and U-Net.
The proposed hybrid ResUNet model exhibited excellent classification accuracy, reflected in the noticeable improvement in all evaluated metrics. His ability to describe complex lesions was particularly outstanding, improving diagnostic accuracy. Our experimental results demonstrate that the proposed Hybrid ResUNet model outperforms existing state-of-the-art methods, achieving an accuracy of 95.8%, a Dice coefficient of 93.1%, and a Jaccard index of 87.5.
The addition of ResUNet to ACO in the proposed Hybrid ResUNet model significantly improves the classification of skin lesions. This integration goes beyond traditional paradigms and demonstrates a viable strategy for deploying AI-powered tools in clinical settings.
Future investigations will focus on increasing the version's abilities by using multi-modal imaging information, experimenting with alternative optimization algorithms, and comparing real-world medical applicability. There is also a promising scope for enhancing computational performance and exploring the model's interpretability for more clinical adoption.
皮肤病变的分割在组织学诊断和皮肤癌监测中仍然至关重要。深度学习的最新进展为医学成像的更大改进铺平了道路。混合残差网络（ResUNet）模型与蚁群优化（ACO）相结合，代表了这些改进的协同作用，旨在提高皮肤病变诊断的效率和效果。
本文旨在评估 Hybrid ResUNet 模型在皮肤病变分类中的有效性，并评估其对优化 ACO 性能的影响，以弥合计算效率和临床实用性之间的差距。
该研究使用深度学习设计在一个包含各种皮肤病变的复杂数据集上进行。该方法包括使用标准参数训练 Hybrid ResUNet 模型，并使用 ACO 进行微调以进行超参数优化。使用传统指标（如准确性、骰子系数和 Jaccard 指数）与现有模型（如残差网络（ResNet）和 U-Net）进行比较来评估性能。
所提出的混合 ResUNet 模型表现出出色的分类准确性，体现在所有评估指标上的显著提高。他描述复杂病变的能力尤为突出，提高了诊断准确性。我们的实验结果表明，所提出的 Hybrid ResUNet 模型优于现有的最先进方法，达到了 95.8%的准确性、93.1%的骰子系数和 87.5%的 Jaccard 指数。
在提出的 Hybrid ResUNet 模型中，将 ResUNet 添加到 ACO 中显著改善了皮肤病变的分类。这种集成超越了传统范例，展示了在临床环境中部署基于人工智能的工具的可行策略。
未来的研究将集中在通过使用多模态成像信息、尝试替代优化算法以及比较实际医疗适用性来提高版本的能力。还有很大的潜力来提高计算性能并探索模型的可解释性，以实现更广泛的临床应用。
