Bouamrane Amira, Derdour Makhlouf, Bennour Akram, Elfadil Eisa Taiseer Abdalla, M Emara Abdel-Hamid, Al-Sarem Mohammed, Kurdi Neesrin Ali
LIAOA Laboratory, University of Oum El-Bouaghi-Larbi Benmhidi, Oum El-Bouaghi 04000, Algeria.
LAMIS Laboratory, Echahid Cheikh Larbi Tebessi University, Tebessa 12002, Algeria.
Diagnostics (Basel). 2024 Dec 24;15(1):1. doi: 10.3390/diagnostics15010001.
Computer-aided diagnostic systems have achieved remarkable success in the medical field, particularly in diagnosing malignant tumors, and have done so at a rapid pace. However, the generalizability of the results remains a challenge for researchers and decreases the credibility of these models, which represents a point of criticism by physicians and specialists, especially given the sensitivity of the field. This study proposes a novel model based on deep learning to enhance lung cancer diagnosis quality, understandability, and generalizability. The proposed approach uses five computed tomography (CT) datasets to assess diversity and heterogeneity. Moreover, the mixup augmentation technique was adopted to facilitate the reliance on salient characteristics by combining features and CT scan labels from datasets to reduce their biases and subjectivity, thus improving the model's generalization ability and enhancing its robustness. Curriculum learning was used to train the model, starting with simple sets to learn complicated ones quickly. The proposed approach achieved promising results, with an accuracy of 99.38%; precision, specificity, and area under the curve (AUC) of 100%; sensitivity of 98.76%; and F1-score of 99.37%. Additionally, it scored a 00% false positive rate and only a 1.23% false negative rate. An external dataset was used to further validate the proposed method's effectiveness. The proposed approach achieved optimal results of 100% in all metrics, with 00% false positive and false negative rates. Finally, explainable artificial intelligence (XAI) using Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to better understand the model. This research proposes a robust and interpretable model for lung cancer diagnostics with improved generalizability and validity. Incorporating mixup and curriculum training supported by several datasets underlines its promise for employment as a diagnostic device in the medical industry.
计算机辅助诊断系统在医学领域取得了显著成功，尤其是在恶性肿瘤诊断方面，而且发展迅速。然而，研究结果的可推广性仍然是研究人员面临的挑战，降低了这些模型的可信度，这受到了医生和专家的批评，特别是考虑到该领域的敏感性。本研究提出了一种基于深度学习的新型模型，以提高肺癌诊断的质量、可理解性和可推广性。所提出的方法使用五个计算机断层扫描（CT）数据集来评估多样性和异质性。此外，采用了混合增强技术，通过组合数据集的特征和CT扫描标签来促进对显著特征的依赖，以减少其偏差和主观性，从而提高模型的泛化能力并增强其鲁棒性。课程学习用于训练模型，从简单的数据集开始，以便快速学习复杂的数据集。所提出的方法取得了令人满意的结果，准确率为99.38%；精确率、特异性和曲线下面积（AUC）为100%；灵敏度为98.76%；F1分数为99.37%。此外，其假阳性率为00%，假阴性率仅为1.23%。使用外部数据集进一步验证了所提出方法的有效性。所提出的方法在所有指标上均取得了100%的最佳结果，假阳性和假阴性率均为00%。最后，使用基于梯度加权类激活映射（Grad-CAM）的可解释人工智能（XAI）来更好地理解模型。本研究提出了一种用于肺癌诊断的强大且可解释的模型，具有更高的可推广性和有效性。结合多个数据集支持的混合增强和课程训练突显了其在医疗行业作为诊断设备应用的前景。
