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Wireless capsule endoscopy (WCE) allows the screening and diagnosis of a patient’s gastrointestinal (GI) tract, including the areas inaccessible to traditional endoscopy, safely and painlessly. However, a large number of images produced in WCE examination require significant time and expertise to process the information and the achieved accuracy is limited due to manual examination by experts. To solve these problems, a novel squeeze-and-excitation (SE) + residual neural network (ResNet)-H GI lesion recognition model is proposed to automate the screening and detection process. An attention mechanism is added to the original ResNet50 model to form a new attention mechanism + ResNet50 model. Then, transfer learning is integrated with the new attention mechanism + ResNet50 model to classify WCE images. The achieved results in experimental settings involving different combinations of positions and types of added attention mechanisms demonstrate that the average detection accuracy of the optimized improved model can reach up to 97.84% for all types of GI images. Subsequently, large-margin cosine loss was used to replace the cross-entropy loss function. Based on the comparative analysis, the improved loss function enhanced the detection accuracy to 98.47% for different types of GI images. Furthermore, the results achieved using the gradient-weighted class activation mapping (Grad-CAM) illustrate that the improved model can capture the focal area well, and promising performance was achieved compared to the original ResNet50 model. The suggested approach can potentially assist endoscopists in the detection and examination of GI diseases during endoscopy.

A novel localization method for wireless capsule endoscope (WCE) based on an improved artificial bee colony (IABC) algorithm is presented. This work contributes to improving the two key aspects of the approach, i.e., optimizing the parameters and improving the search strategy. The experimental results achieved in this work are compared with the Levenberg–Marquardt (LM) algorithm, artificial bee colony (ABC) algorithm, genetic algorithm (GA), and particle swarm optimization (PSO) algorithm. Compared with the GA algorithm, ABC algorithm and PSO algorithm, the IABC algorithm achieved significant improvement in terms of the convergence speed, convergence time and running time, and average relative error. For instance, the average relative error of the IABC algorithm was reduced by 3.48%, 1.47%, 2.79%, and 1.97%, compared with the GA algorithm, ABC algorithm, PSO algorithm, and LM algorithm, respectively. In this work, experiments were also performed to validate the feasibility and accuracy of the proposed algorithm. Overall, promising results were achieved in this work and outcomes play a significant role in realizing WCE closed-loop active control, biopsy, and administration in the future.

Sleep disorders form a massive global health burden and there is an increasing need for simple and cost-efficient sleep recording devices. Recent machine learning–based approaches have already achieved scoring accuracy of sleep recordings on par with manual scoring, even with decreased recording montages. Simple and inexpensive monitoring over multiple consecutive nights with automatic analysis could be the answer to overcome the substantial economic burden caused by poor sleep and enable more efficient initial diagnosis, treatment planning, and follow-up monitoring for individuals suffering from sleep disorders.