Gastrointestinal (GI) disease is one of the most common diseases and primarily examined by GI endoscopy. Recently, deep learning (DL), in particular convolutional neural networks (CNNs) have made achievements in GI endoscopy image analysis. This review focuses on the applications of DL methods in the analysis of GI images. We summarized and compared the latest published literature related to the common clinical GI diseases and covers the key applications of DL in GI image detection, classification, segmentation, recognition, location, and other tasks. At the end, we give a discussion on the challenges and the research directions of GI image analysis based on DL in the future.

The accurate diagnosis of various esophageal diseases at different stages is crucial for providing precision therapy planning and improving 5-year survival rate of esophageal cancer patients. Automatic classification of various esophageal diseases in gastroscopic images can assist doctors to improve the diagnosis efficiency and accuracy. The existing deep learning-based classification method can only classify very few categories of esophageal diseases at the same time. Hence, we proposed a novel efficient channel attention deep dense convolutional neural network (ECA-DDCNN), which can classify the esophageal gastroscopic images into four main categories including normal esophagus (NE), precancerous esophageal diseases (PEDs), early esophageal cancer (EEC) and advanced esophageal cancer (AEC), covering six common sub-categories of esophageal diseases and one normal esophagus (seven sub-categories). In total, 20,965 gastroscopic images were collected from 4,077 patients and used to train and test our proposed method. Extensive experiments results have demonstrated convincingly that our proposed ECA-DDCNN outperforms the other state-of-art methods. The classification accuracy (Acc) of our method is 90.63% and the averaged area under curve (AUC) is 0.9877. Compared with other state-of-art methods, our method shows better performance in the classification of various esophageal disease. Particularly for these esophageal diseases with similar mucosal features, our method also achieves higher true positive (TP) rates. In conclusion, our proposed classification method has confirmed its potential ability in a wide variety of esophageal disease diagnosis.

Summary In this paper, the L1 filtering problem is studied for continuous‐time switched positive linear systems (SPLSs) with a small delay existing in the switching of the filter and the subsystem. Unlike the existing literature concerned with asynchronous problems of SPLSs, the synchronous and asynchronous filters will be designed separately, which implies less conservative results. By introducing a class of clock‐dependent Lyapunov function (CDLF), which jumps down when the modes of the filter or the subsystem change and may increase or decrease during the asynchronous interval, clock‐dependent sufficient conditions characterizing a nonweighted L1‐gain performance of the filter error systems are established. Then, based on the L1 analysis results, a pair of error‐bounding filters are designed to estimate the outputs of SPLSs. The filter gains can be obtained by solving a set of linear programming. Finally, two numerical examples are presented to show the effectiveness and advantages of the results.

Gastrointestinal (GI) diseases constitute a leading problem in the human digestive system. Consequently, several studies have explored automatic classification of GI diseases as a means of minimizing the burden on clinicians and improving patient outcomes, for both diagnostic and treatment purposes. The challenge in using deep learning-based (DL) approaches, specifically a convolutional neural network (CNN), is that spatial information is not fully utilized due to the inherent mechanism of CNNs. This paper proposes the application of spatial factors in improving classification performance. Specifically, we propose a deep CNN-based spatial attention mechanism for the classification of GI diseases, implemented with encoder–decoder layers. To overcome the data imbalance problem, we adapt data-augmentation techniques. A total of 12,147 multi-sited, multi-diseased GI images, drawn from publicly available and private sources, were used to validate the proposed approach. Furthermore, a five-fold cross-validation approach was adopted to minimize inconsistencies in intra- and inter-class variability and to ensure that results were robustly assessed. Our results, compared with other state-of-the-art models in terms of mean accuracy (ResNet50 = 90.28, GoogLeNet = 91.38, DenseNets = 91.60, and baseline = 92.84), demonstrated better outcomes (Precision = 92.8, Recall = 92.7, F1-score = 92.8, and Accuracy = 93.19). We also implemented t-distributed stochastic neighbor embedding (t–SNE) and confusion matrix analysis techniques for better visualization and performance validation. Overall, the results showed that the attention mechanism improved the automatic classification of multi-sited GI disease images. We validated clinical tests based on the proposed method by overcoming previous limitations, with the goal of improving automatic classification accuracy in future work.

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The early diagnoses of esophageal cancer are of great significance in the clinic because they are critical for reducing mortality. At present, the diagnoses are mainly performed by artificial detection and annotations based on gastroscopic images. However, these procedures are very challenging to clinicians due to the large variability in the appearance of early cancer lesions. To reduce the subjectivity and fatigue in manual annotations and to improve the efficiency of diagnoses, computer-aided annotation methods are highly required. In this work, we proposed a novel method that utilized deep learning (DL) techniques to realize the automatic annotation of early esophageal cancer (EEC) lesions in gastroscopic images. The depth map of gastroscopic images was initially extracted by a DL network. Then, this additional depth information was fused with the original RGB gastroscopic images, which were then sent to another DL network to obtain precise annotations of EEC regions. In total, 4231 gastroscopic images of 732 patients were used to build and validate the proposed method. A total of 3190 of those images were EEC images, and the remaining 1041 were non-EEC images. The experimental results show that the combination of depth information and RGB information improved the annotation performance. The final EEC detection rate and mean Dice Similarity Coefficient (DSC) of our method were 97.54% and 74.43%, respectively. Compared with other state-of-the-art DL-based methods, the proposed method showed better annotation performances and fewer false positive outputs. Therefore, our method offers a good prospect in aiding the clinical diagnoses of EEC.INDEX TERMS Gastroscopic image, early esophageal cancer, lesion annotation, deep learning, depth map.