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Detection of abnormalities in wireless capsule endoscopy (WCE) images is a challenging task. Typically, these images suffer from low contrast, complex background, variations in lesion shape and color, which affect the accuracy of their segmentation and subsequent classification. This research proposes an automated system for detection and classification of ulcers in WCE images, based on state-of-the-art deep learning networks. Deep learning techniques, and in particular, convolutional neural networks (CNNs), have recently become popular in the analysis and recognition of medical images. The medical image datasets used in this study were obtained from WCE video frames. In this work, two milestone CNN architectures, namely the AlexNet and the GoogLeNet are extensively evaluated in object classification into ulcer or non-ulcer. Furthermore, we examine and analyze the images identified as containing ulcer objects to evaluate the efficiency of the utilized CNNs. Extensive experiments show that CNNs deliver superior performance, surpassing traditional machine learning methods by large margins, which supports their effectiveness as automated diagnosis tools.

This research proposes a miniature circular polarization antenna used in a wireless capsule endoscopy system at 2.45 GHz for industrial, scientific, and medical bands. We propose a method of cutting a chamfer rectangular slot on a circular radiation patch and introducing a curved radiation structure into the centerline position of the chamfer rectangular slot, while a short-circuit probe is added to achieve miniaturization. Therefore, we significantly reduced the size of the antenna and made it exhibit circularly polarized radiation characteristics. A cross-slot is cut in the GND to enable the antenna to better cover the operating band while being able to meet the complex human environment. The effective axis ratio bandwidth is 120 MHz (2.38–2.50 GHz). Its size is π × 0.032λ02 × 0.007λ0 (where λ0 is the free-space wavelength of at 2.4 GHz). In addition, the effect of different organs such as muscle, stomach, small intestine, and big intestine on the antenna when it was embedded into the wireless capsule endoscopy (WCE) system was further discussed, and the results proved that the WCE system has better robustness in different organs. The antenna’s specific absorption rate can follow the IEEE Standard Safety Guidelines (IEEE C95.1-1999). A prototype is fabricated and measured. The experimental results are consistent with the simulation results.

Wireless capsule endoscopy (WCE) examinations generate approximately 55,000 images per procedure, with a vast majority being redundant due to high structural similarity, imposing a significant burden on physicians during review. This paper introduces MSRCTNet, a novel Multi-Scale Capsule Triplet Network, to efficiently remove redundant frames while preserving clinically essential information. By addressing key challenges such as data imbalance, small sample sizes, and the need for balanced accuracy and efficiency, MSRCTNet enhances feature extraction through multi-scale processing and attention mechanisms, refines representations via capsule networks, and assesses frame similarity using an optimized triplet framework. Evaluated on a custom dataset of 257,362 WCE images (360 360 resolution) from the First Affiliated Hospital of Yangtze University, Jingzhou, China, MSRCTNet achieves 96.1% accuracy in redundancy removal, with a false detection rate of 2.84%, missing detection rate of 0.19%, and real-time processing at 0.02 seconds per frame. These advancements not only reduce physician workload and fatigue but also demonstrate superior robustness and adaptability for clinical applications, outperforming existing methods in handling diverse endoscopic scenarios.

Conventional endoscopy is limited in its ability to examine the small bowel and perform long-term monitoring due to the risk of infection and tissue perforation. Wireless Capsule Endoscopy (WCE) is a painless and non-invasive method of examining the body’s internal organs using a small camera that is swallowed like a pill. The existing active locomotion technologies do not have a practical localization system to control the capsule’s movement within the body. A robust localization system is essential for safely guiding the WCE device through the complex gastrointestinal (GI) tract. Moreover, having access to the capsule’s trajectory data is highly desirable for drug delivery and surgery, as well as for creating accurate user profiles for diagnosis and future reference. Therefore, a robust, real-time, and practical localization system is imperative to advance the field of WCE and make it desirable for clinical trials. In this work, we have identified salient features of different localization techniques and categorized studies in comprehensive tables. This study is self-contained as it offers a comprehensive overview of emerging localization techniques based on magnetic field, radio frequency (RF), video, and hybrid methods. A summary at the end of each method is provided to point out the potential gaps and give directions for future research. The main point of this work is to present an in-depth review of the most recent localization techniques published in the past five years. This will assist researchers in comprehending current techniques and pinpointing potential areas for further investigation. This review can be a significant reference and guide for future research on WCE localization.

Capsule endoscopy (CE) is a widely used medical imaging tool for the diagnosis of gastrointestinal tract abnormalities like bleeding. However, CE captures a huge number of image frames, constituting a time-consuming and tedious task for medical experts to manually inspect. To address this issue, researchers have focused on computer-aided bleeding detection systems to automatically identify bleeding in real time. This paper presents a systematic review of the available state-of-the-art computer-aided bleeding detection algorithms for capsule endoscopy. The review was carried out by searching five different repositories (Scopus, PubMed, IEEE Xplore, ACM Digital Library, and ScienceDirect) for all original publications on computer-aided bleeding detection published between 2001 and 2023. The Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) methodology was used to perform the review, and 147 full texts of scientific papers were reviewed. The contributions of this paper are: (I) a taxonomy for computer-aided bleeding detection algorithms for capsule endoscopy is identified; (II) the available state-of-the-art computer-aided bleeding detection algorithms, including various color spaces (RGB, HSV, etc.), feature extraction techniques, and classifiers, are discussed; and (III) the most effective algorithms for practical use are identified. Finally, the paper is concluded by providing future direction for computer-aided bleeding detection research.

This paper proposes a novel 3D discrete cosine transform (DCT) based image compression method for medical endoscopic applications. Due to the high correlation among color components of wireless capsule endoscopy (WCE) images, the original 2D Bayer data pattern is reconstructed into a new 3D data pattern, and 3D DCT is adopted to compress the 3D data for high compression ratio and high quality. For the low computational complexity of 3D-DCT, an optimized 4-point DCT butterfly structure without multiplication operation is proposed. Due to the unique characteristics of the 3D data pattern, the quantization and zigzag scan are ameliorated. To further improve the visual quality of decompressed images, a frequency-domain filter is proposed to eliminate the blocking artifacts adaptively. Experiments show that our method attains an average compression ratio (CR) of 22.94:1 with the peak signal to noise ratio (PSNR) of 40.73 dB, which outperforms state-of-the-art methods.

Worldwide, cancer is one of the leading causes of death in humans. Interobserver variability and specialized experience are key factors in diagnosing gastrointestinal tract (GIT) abnormalities using endoscopic procedures. Due to this diversity, small lesions may go unnoticed, leading to a delay in early diagnosis. Therefore, it is essential to design a computer-aided diagnosis (CAD) system for the detection and classification of GIT diseases at the early stages. This paper proposes a CAD system that combines the feature fusion of modified deep learning models with optimal feature selection. Three publicly available datasets, including Kvasir V1, Kvasir V2, and Hyperkvasir, are utilized in the experimental process. In the proposed method, a contrast enhancement step is performed using the fusion of the top-bottom filtering technique. In the next step, two deep learning models (ResNet18 and ResNet50) are modified with a new layer called entropic field propagation (EFP). The pooling layers are replaced with EFP layers in both models, which are then trained on the selected datasets. In the testing process, trained models are employed, and features are extracted from the deeper layers, which are further refined using the Newton-Raphson Marine Predator Optimization (NRMPO) algorithm. The selected features from both models are finally fused using a novel mean threshold-based fusion approach and passed to machine learning classifiers. The proposed CAD system achieved accuracies of 99.0, 89.6, and 82.7% for Kvasir V1, Kvasir V2, and HyperKvasir, respectively. A detailed ablation study is also conducted for the middle steps that validate these reported accuracies. C onclusion : A comparison is performed with state-of-the-art (SOTA) techniques, showing that the proposed method achieves improved accuracy and precision rates.

Wireless capsule endoscopy (WCE) has been widely used in gastrointestinal (GI) diagnosis that allows the physicians to examine the interior wall of the human GI tract through a pain-free procedure. However, there are still several limitations of the technology, which limits its functionality, ultimately limiting its wide acceptance. Its counterpart, the wired endoscopic system is a painful procedure that demotivates patients from going through the procedure, and adversely affects early diagnosis. Furthermore, the current generation of capsules is unable to automate the detection of abnormality. As a result, physicians are required to spend longer hours to examine each image from the endoscopic capsule for abnormalities, which makes this technology tiresome and error-prone. Early detection of cancer is important to improve the survival rate in patients with colorectal cancer. Hence, a fluorescence-imaging-based endoscopic capsule that automates the detection process of colorectal cancer was designed and developed in our lab. The proof of concept of this endoscopic capsule was tested on porcine intestine and liquid phantom. The proposed WCE system offers great possibilities for future applicability in selective and specific detection of other fluorescently labelled cancers.

Wireless capsule endoscopy is a non-invasive and painless procedure to examine the gastrointestinal tract of human body, and an experienced clinician takes 2–3 hours for complete examination. To reduce this diagnosis time, the present work proposes a lightweight CNN model for binary classification of WCE images. The proposed model has a strong backbone of CNN in the primary branch complemented by resolution preserving dilated convolution layers in secondary branches. The proposed model extracts multiple features at different scales and finally fuses them together to fetch the dominant global feature that aids in binary classification problem. A new dataset has been created in collaboration with All India Institute of Medical Sciences, Delhi. The efficacy of the proposed model has been verified using the developed dataset using various subjective and objective parameters. Feature maps generated at each branch have been thoroughly analyzed to understand the quality of learning. Thorough experimental analysis indicates that the proposed model yields an accuracy of 0.96, sensitivity of 0.93 and specificity of 0.97 on real data collected from AIIMS Delhi. To verify the efficacy of the proposed dilated CNN, extensive analysis has been done using standard KID dataset as well. For a fair comparison, these datasets have also been used for pre-trained inception net model. Thorough analysis indicates that the proposed architecture performs well both for AIIMS dataset and the standard KID dataset. Result analysis also reflects that the proposed dilated CNN architecture outperforms the performance of pre-trained inception net model.