利用 DenseNet 和 CNN 进行数据融合和移动边缘计算,增强肺癌诊断能力

Chengping Zhang, Muhammad Aamir, Yurong Guan, Muna Al-Razgan, Emad Mahrous Awwad, Rizwan Ullah, Uzair Aslam Bhatti, Yazeed Yasin Ghadi
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摘要

最近,通过在计算机断层扫描(CT)上应用卷积神经网络(CNN),在肺癌自动诊断方面取得的进展标志着医学成像和诊断领域的重大飞跃。这些基于 CNN 的分类器在检测和分析肺癌症状方面的精确性为早期检测和治疗规划开辟了新途径。然而,尽管取得了这些技术进步,但仍有一些关键领域需要进一步探索和发展。在这一背景下,计算机辅助诊断系统和人工智能,特别是区域建议网络、双路径网络和局部二元模式等深度学习方法,已变得举足轻重。然而,这些方法面临着可解释性有限、数据可变性处理问题和概括性不足等挑战。应对这些挑战是提高早期检测和准确诊断的关键,也是制定有效治疗计划和改善患者预后的基础。本研究介绍了一种结合卷积神经网络(CNN)和 DenseNet 的先进方法,利用数据融合和移动边缘计算进行肺癌识别和分类。数据融合技术的集成使系统能够整合来自多个来源的信息,从而提高模型的稳健性和准确性。移动边缘计算可使计算资源更接近数据源,从而加快 CT 扫描图像的处理和分析,这对实时应用至关重要。图像经过预处理,包括调整大小和重新缩放,以优化特征提取。DenseNet-CNN 模型在数据融合和边缘计算能力的加强下,能够从这些 CT 扫描图像中提取和学习特征,有效区分健康肺组织和癌变肺组织。分类类别包括正常、良性和恶性,后者又进一步细分为腺癌、鳞状细胞癌和大细胞癌。在对照实验中,这种方法优于现有的先进方法,准确率高达 99%,令人印象深刻。这表明它有潜力成为肺癌早期检测和分类的有力工具,是医学成像和诊断技术的一大进步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing lung cancer diagnosis with data fusion and mobile edge computing using DenseNet and CNN
The recent advancements in automated lung cancer diagnosis through the application of Convolutional Neural Networks (CNN) on Computed Tomography (CT) scans have marked a significant leap in medical imaging and diagnostics. The precision of these CNN-based classifiers in detecting and analyzing lung cancer symptoms has opened new avenues in early detection and treatment planning. However, despite these technological strides, there are critical areas that require further exploration and development. In this landscape, computer-aided diagnostic systems and artificial intelligence, particularly deep learning methods like the region proposal network, the dual path network, and local binary patterns, have become pivotal. However, these methods face challenges such as limited interpretability, data variability handling issues, and insufficient generalization. Addressing these challenges is key to enhancing early detection and accurate diagnosis, fundamental for effective treatment planning and improving patient outcomes. This study introduces an advanced approach that combines a Convolutional Neural Network (CNN) with DenseNet, leveraging data fusion and mobile edge computing for lung cancer identification and classification. The integration of data fusion techniques enables the system to amalgamate information from multiple sources, enhancing the robustness and accuracy of the model. Mobile edge computing facilitates faster processing and analysis of CT scan images by bringing computational resources closer to the data source, crucial for real-time applications. The images undergo preprocessing, including resizing and rescaling, to optimize feature extraction. The DenseNet-CNN model, strengthened by data fusion and edge computing capabilities, excels in extracting and learning features from these CT scans, effectively distinguishing between healthy and cancerous lung tissues. The classification categories include Normal, Benign, and Malignant, with the latter further sub-categorized into adenocarcinoma, squamous cell carcinoma, and large cell carcinoma. In controlled experiments, this approach outperformed existing state-of-the-art methods, achieving an impressive accuracy of 99%. This indicates its potential as a powerful tool in the early detection and classification of lung cancer, a significant advancement in medical imaging and diagnostic technology.
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