Jim Jamin Rahman, Rayed Md Eshmam, Mridha M F, Nur Kamruddin
Department of Computer Science, American International University-Bangladesh, Dhaka 1229, Bangladesh.
PLoS One. 2025 May 30;20(5):e0322488. doi: 10.1371/journal.pone.0322488. eCollection 2025.
Lung cancer imaging plays a crucial role in early diagnosis and treatment, where machine learning and deep learning have significantly advanced the accuracy and efficiency of disease classification. This study introduces the Explainable and Lightweight Lung Cancer Net (XLLC-Net), a streamlined convolutional neural network designed for classifying lung cancer from histopathological images. Using the LC25000 dataset, which includes three lung cancer classes and two colon cancer classes, we focused solely on the three lung cancer classes for this study. XLLC-Net effectively discerns complex disease patterns within these classes. The model consists of four convolutional layers and contains merely 3 million parameters, considerably reducing its computational footprint compared to existing deep learning models. This compact architecture facilitates efficient training, completing each epoch in just 60 seconds. Remarkably, XLLC-Net achieves a classification accuracy of 99.62% [Formula: see text] 0.16%, with precision, recall, and F1 score of 99.33% [Formula: see text] 0.30%, 99.67% [Formula: see text] 0.30%, and 99.70% [Formula: see text] 0.30%, respectively. Furthermore, the integration of Explainable AI techniques, such as Saliency Map and GRAD-CAM, enhances the interpretability of the model, offering clear visual insights into its decision-making process. Our results underscore the potential of lightweight DL models in medical imaging, providing high accuracy and rapid training while ensuring model transparency and reliability.
肺癌成像在早期诊断和治疗中起着至关重要的作用，机器学习和深度学习显著提高了疾病分类的准确性和效率。本研究介绍了可解释轻量级肺癌网络（XLLC-Net），这是一种为从组织病理学图像中分类肺癌而设计的简化卷积神经网络。使用包含三个肺癌类别和两个结肠癌类别的LC25000数据集，本研究仅关注三个肺癌类别。XLLC-Net有效地识别了这些类别中的复杂疾病模式。该模型由四个卷积层组成，仅包含300万个参数，与现有的深度学习模型相比，大大减少了其计算量。这种紧凑的架构便于高效训练，每个epoch仅需60秒即可完成。值得注意的是，XLLC-Net的分类准确率达到99.62% [公式：见正文] 0.16%，精确率、召回率和F1分数分别为99.33% [公式：见正文] 0.30%、99.67% [公式：见正文] 0.30%和99.70% [公式：见正文] 0.30%。此外，显著图和梯度加权类激活映射等可解释人工智能技术的集成增强了模型的可解释性，为其决策过程提供了清晰的视觉洞察。我们的结果强调了轻量级深度学习模型在医学成像中的潜力，在确保模型透明度和可靠性的同时提供了高精度和快速训练。
