Accurate identification of Hepatitis B virus (HBV) disease by analyzing the Raman spectroscopic images is a challenge for pathologists. To save precious human lives, an efficient technique is required with higher diagnostic accuracy at early‐stage of HBV. We proposed a novel method of HBV diagnosis using deep neural networks with the concept of transfer learning and Raman spectroscopic images. The proposed approach developed by utilizing pretrained convolutional neural networks ResNet101 by employing transfer learning on a real dataset of HBV‐infected blood plasma samples. Dataset consists of 1000 Raman images in which 600 are HBV‐infected blood plasma samples, and 400 are healthy ones. The developed model is capable to detect minute variation between infected and healthy samples and achieved enhanced performance. The proposed approach has been assessed and attained high classification accuracy, sensitivity, specificity, and AUC of 99.7%, 100%, 99.25%, and 98.7%, respectively. The proposed TL‐ResNet101 model outperformed the conventional approaches such as PCA‐SVM and PCA‐LDA and demonstrated improved accuracy more than 7%. High performance indicates that the developed TL‐ResNet101 model has potential to use for HBV diagnosis. Moreover, the developed automated approach can be extended for other disease.

中文翻译：

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使用光谱图像基于深度转移学习的乙型肝炎病毒诊断

通过分析拉曼光谱图像来准确识别乙型肝炎病毒（HBV）疾病是病理学家的一个挑战。为了挽救宝贵的生命，在HBV早期阶段需要一种具有更高诊断准确性的有效技术。我们提出了一种使用深度神经网络的HBV诊断新方法，并具有转移学习和拉曼光谱图像的概念。利用预训练卷积神经网络ResNet101开发的建议方法通过对HBV感染的血浆样本的真实数据集进行转移学习。数据集由1000张拉曼图像组成，其中600张是HBV感染的血浆样本，而400张是健康的。开发的模型能够检测受感染和健康样品之间的微小差异，并获得增强的性能。评估了所提出的方法，并获得了99.7％，100％，99.25％和98.7％的高分类准确性，敏感性，特异性和AUC。拟议的TL-ResNet101模型优于常规方法（如PCA-SVM和PCA-LDA），并显示出超过7％的改进精度。高性能表明已开发TL-ResNet101该模型具有用于HBV诊断的潜力。而且，开发的自动化方法可以扩展到其他疾病。