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Abstract：
Celiac Disease (CD) is a primary malabsorption syndrome resulting from the interplay of genetic, immune, and dietary factors. CD negatively impacts daily activities and may lead to conditions such as osteoporosis, malignancies in the small intestine, ulcerative jejunitis, and enteritis, ultimately causing severe malnutrition. Therefore, an effective and rapid differentiation between healthy individuals and those with celiac disease is crucial for early diagnosis and treatment. This study utilizes Raman spectroscopy combined with deep learning models to achieve a non-invasive, rapid, and accurate diagnostic method for celiac disease and healthy controls. A total of 59 plasma samples, comprising 29 celiac disease cases and 30 healthy controls, were collected for experimental purposes. Convolutional Neural Network (CNN), Multi-Scale Convolutional Neural Network (MCNN), Residual Network (ResNet), and Deep Residual Shrinkage Network (DRSN) classification models were employed. The accuracy rates for these models were found to be 86.67%, 90.76%, 86.67% and 95.00%, respectively. Comparative validation results revealed that the DRSN model exhibited the best performance, with an AUC value and accuracy of 97.60% and 95%, respectively. This confirms the superiority of Raman spectroscopy combined with deep learning in the diagnosis of celiac disease.
摘要：
乳糜泻（CD）是一种由遗传相互作用引起的原发性吸收不良综合征，免疫，和饮食因素。CD对日常活动产生负面影响，并可能导致骨质疏松症等疾病，小肠恶性肿瘤，溃疡性骨髓炎,和肠炎,最终导致严重的营养不良。因此,健康个体和乳糜泻患者之间的有效和快速的区别对于早期诊断和治疗至关重要。本研究利用拉曼光谱与深度学习模型相结合，实现了非侵入性、快速,乳糜泻和健康对照的准确诊断方法。总共59个血浆样本,包括29例乳糜泻病例和30例健康对照，被收集用于实验目的。卷积神经网络(CNN)多尺度卷积神经网络(MCNN)剩余网络(ResNet),采用深度残差收缩网络(DRSN)分类模型。这些模型的准确率为86.67%，90.76%,86.67%和95.00%，分别。对比验证结果表明，DRSN模型表现出最佳性能，AUC值和准确度分别为97.60%和95%，分别。这证实了拉曼光谱结合深度学习在乳糜泻诊断中的优越性。
