Wireless capsule endoscopy (WCE) might move through human body and captures the small bowel and captures the video and require the analysis of all frames of video due to which the diagnosis of gastrointestinal infections by the physician is a tedious task. This tiresome assignment has fuelled the researcher’s efforts to present an automated technique for gastrointestinal infections detection. The segmentation of stomach infections is a challenging task because the lesion region having low contrast and irregular shape and size. To handle this challenging task, in this research work a new deep semantic segmentation model is suggested for 3D-segmentation of the different types of stomach infections. In the segmentation model, deep labv3 is employed as a backbone of the ResNet-50 model. The model is trained with ground-masks and accurately performs pixel-wise classification in the testing phase. Similarity among the different types of stomach lesions accurate classification is a difficult task, which is addressed in this reported research by extracting deep features from global input images using a pre-trained ResNet-50 model. Furthermore, the latest advances in the estimation of uncertainty and model interpretability in the classification of different types of stomach infections is presented. The classification results estimate uncertainty related to the vital features in input and show how uncertainty and interpretability might be modeled in ResNet-50 for the classification of the different types of stomach infections. The proposed model achieved up to 90% prediction scores to authenticate the method performance.

无线胶囊内窥镜检查（WCE）可能会在人体中移动并捕获小肠并捕获视频，并需要对视频的所有帧进行分析，因此，由医生诊断胃肠道感染是一项繁琐的任务。这项艰巨的任务为研究人员提供了一种用于胃肠道感染检测的自动化技术，从而助长了他们的努力。胃感染的分割是一项艰巨的任务，因为病变区域的对比度低，形状和大小不规则。为了处理这一具有挑战性的任务，在这项研究工作中，提出了一种新的深度语义分割模型，用于对不同类型的胃部感染进行3D分割。在细分模型中，深度labv3被用作ResNet-50模型的骨干。该模型使用地面掩膜进行训练，并在测试阶段准确执行像素级分类。在不同类型的胃部病变之间进行相似性，准确分类是一项艰巨的任务，在此报道的研究中，这是通过使用预先训练的ResNet-50模型从全局输入图像中提取深层特征来解决的。此外，在对不同类型的胃部感染进行分类的不确定性和模型可解释性的估计方面，也提出了最新的进展。分类结果估计了与输入中的重要特征相关的不确定性，并显示了如何在ResNet-50中对不确定性和可解释性进行建模，以对不同类型的胃部感染进行分类。所提出的模型获得了高达90％的预测分数，以验证该方法的性能。