Wireless Capsule Endoscopy Images Research Articles

A Robust Approach for Ulcer Classification/Detection in WCE Images

Wireless Capsule Endoscopy (WCE) is a medical diagnostic technique recognized for its minimally invasive and painless nature for the patients. It uses remote imaging techniques to explore various segments of the gastrointestinal (GI) tract, particularly the hard-to-reach small intestine, making it an effective alternative to traditional endoscopic techniques. However, physicians face a significant challenge when it comes to analyzing a large number of endoscopic images due to the effort and time required. It is therefore imperative to implement aided-diagnostic systems capable of automatically detecting suspicious areas for subsequent medical assessment. In this paper, we present a novel approach to identify gastrointestinal tract abnormalities from WCE images, with a particular focus on ulcerated areas. Our approach involves the use of the Median Robust Extended Local Binary Pattern (MRELBP) descriptor, which effectively overcomes the challenges faced when WCE image acquisition, such as variations in illumination and contrast, rotation, and noise. Using machine learning algorithms, we conducted experiments on the extensive Kvasir-Capsule dataset, and subsequently compared our results with recent relevant studies. Noteworthy is the fact that our approach achieved an accuracy of 97.04% with the SVM (RBF) classifier and 96.77% with the RF classifier.

Review of Deep Learning Performance in Wireless Capsule Endoscopy Images for GI Disease Classification

Wireless capsule endoscopy is a non-invasive medical imaging modality used for diagnosing and monitoring digestive tract diseases. However, the analysis of images obtained from wireless capsule endoscopy is a challenging task, as the images are of low resolution and often contain a large number of artifacts. In recent years, deep learning has shown great promise in the analysis of medical images, including wireless capsule endoscopy images. This paper provides a review of the current trends and future directions in deep learning for wireless capsule endoscopy. We focus on the recent advances in transfer learning, attention mechanisms, multi-modal learning, automated lesion detection, interpretability and explainability, data augmentation, and edge computing. We also highlight the challenges and limitations of current deep learning methods and discuss the potential future directions for the field. Our review provides insights into the ongoing research and development efforts in the field of deep learning for wireless capsule endoscopy, and can serve as a reference for researchers, clinicians, and engineers working in this area inspection process.

Efficient-gastro: optimized EfficientNet model for the detection of gastrointestinal disorders using transfer learning and wireless capsule endoscopy images.

Gastrointestinal diseases cause around two million deaths globally. Wireless capsule endoscopy is a recent advancement in medical imaging, but manual diagnosis is challenging due to the large number of images generated. This has led to research into computer-assisted methodologies for diagnosing these images. Endoscopy produces thousands of frames for each patient, making manual examination difficult, laborious, and error-prone. An automated approach is essential to speed up the diagnosis process, reduce costs, and potentially save lives. This study proposes transfer learning-based efficient deep learning methods for detecting gastrointestinal disorders from multiple modalities, aiming to detect gastrointestinal diseases with superior accuracy and reduce the efforts and costs of medical experts. The Kvasir eight-class dataset was used for the experiment, where endoscopic images were preprocessed and enriched with augmentation techniques. An EfficientNet model was optimized via transfer learning and fine tuning, and the model was compared to the most widely used pre-trained deep learning models. The model's efficacy was tested on another independent endoscopic dataset to prove its robustness and reliability.

Color-Transfer-Enhanced Data Construction and Validation for Deep Learning-Based Upper Gastrointestinal Landmark Classification in Wireless Capsule Endoscopy.

While the adoption of wireless capsule endoscopy (WCE) has been steadily increasing, its primary application remains limited to observing the small intestine, with relatively less application in the upper gastrointestinal tract. However, there is a growing anticipation that advancements in capsule endoscopy technology will lead to a significant increase in its application in upper gastrointestinal examinations. This study addresses the underexplored domain of landmark identification within the upper gastrointestinal tract using WCE, acknowledging the limited research and public datasets available in this emerging field. To contribute to the future development of WCE for gastroscopy, a novel approach is proposed. Utilizing color transfer techniques, a simulated WCE dataset tailored for the upper gastrointestinal tract is created. Using Euclidean distance measurements, the similarity between this color-transferred dataset and authentic WCE images is verified. Pioneering the exploration of anatomical landmark classification with WCE data, this study integrates similarity evaluation with image preprocessing and deep learning techniques, specifically employing the DenseNet169 model. As a result, utilizing the color-transferred dataset achieves an anatomical landmark classification accuracy exceeding 90% in the upper gastrointestinal tract. Furthermore, the application of sharpen and detail filters demonstrates an increase in classification accuracy from 91.32% to 94.06%.

Abnormalities detection from wireless capsule endoscopy images based on embedding learning with triplet loss

Abnormalities detection from wireless capsule endoscopy images based on embedding learning with triplet loss

Design and development of artificial intelligence‐based application programming interface for early detection and diagnosis of colorectal cancer from wireless capsule endoscopy images

AbstractThe colorectal cancer (CRC) is gaining attention in the context of gastrointestinal tract diseases as it ranks third among the most prevalent type of cancer. The early diagnosis of the CRC can be done by periodic examination of the colon and rectum for innocuous tissue abnormality called polyp as it has the potential to evolve as malignant in future. The CRC diagnosis using wireless capsule endoscopy requires the dedicated commitment of the medical expert demanding significant time, focus and effort. The accuracy of manual analysis in identifying polyps is extensively reliant on the cognitive condition of the physician, thus emphasizing the requirement for automatic polyp identification. The artificial intelligence integrated computer‐aided diagnosis system could assist the clinician in better diagnosis, thereby reducing the miss‐rates of polyps. In our proposed study, we developed an application program interface to aid the clinician in automatic segmentation of the polyp and evaluation of its dimension by manual placement of four landmarks on the predicted polyp. The segmentation is performed by the proposed light weight Padded U‐Net for the effective polyp segmentation in the colorectal images. We trained and validated the Padded U‐Net with augmented images of Kvasir dataset and calculated the performance parameters. In order to facilitate image augmentation, a graphical user interface called Augment Tree was developed, which incorporates 92 augmentation techniques. The accuracy, recall, precision, IoU, F1‐score, loss achieved during validation of Padded U‐Net were 95.6%, 0.946%, 0.985%, 0.933%, 0.965% and 0.080% respectively. We demonstrated that accuracy was improved and loss was reduced when the model was trained with augmented images rather than only the limited original dataset images. On comparison of our Padded U‐net architecture with recently developed architectures, our model attained optimal performance in all the metrics except accuracy in which it attained marginal performance to the highest value.

Multi-classification deep learning models for detection of ulcerative colitis, polyps, and dyed-lifted polyps using wireless capsule endoscopy images

Wireless capsule endoscopy (WCE) enables imaging and diagnostics of the gastrointestinal (GI) tract to be performed without any discomfort. Despite this, several characteristics, including efficacy, tolerance, safety, and performance, make it difficult to apply and modify widely. The use of automated WCE to collect data and perform the analysis is essential for finding anomalies. Medical specialists need a significant amount of time and expertise to examine the data generated by WCE imaging of the patient’s digestive tract. To address these challenges, several computer vision-based solutions have been designed; nevertheless, they do not achieve an acceptable level of accuracy, and more advancements are required. Thus, in this study, we proposed four multi-classification deep learning (DL) models i.e., Vgg-19 + CNN, ResNet152V2, Gated Recurrent Unit (GRU) + ResNet152V2, and ResNet152V2 + Bidirectional GRU (Bi-GRU) and applied it on different publicly available databases for diagnosing ulcerative colitis, polyps, and dyed-lifted polyps using WCE images. To our knowledge, this is the only study that uses a single DL model for the classification of three different GI diseases. We compared the classification performance of the proposed DL classifiers in terms of many parameters such as accuracy, loss, Matthew's correlation coefficient (MCC), recall, precision, negative predictive value (NPV), positive predictive value (PPV), and F1-score. The results revealed that the Vgg-19 + CNN outperforms the three other proposed DL models in classifying GI diseases using WCE images. The Vgg-19 + CNN model achieved an accuracy of 99.45%. The results of four proposed DL classifiers are also compared with recent state-of-the-art classifiers and the proposed Vgg-19 + CNN model has performed better in terms of improved accuracy.

Feature Extraction for Polyp Detection in Wireless Capsule Endoscopy Video Frames

Wireless capsule endoscopy (WCE) is a technology for filming the gastrointestinal (GI) tract to find abnormalities such as tumors, polyps, and bleeding. This paper proposes a new method based on hand-crafted features to detect polyps in WCE frames. A polyp has a convex surface containing pixel values with a specified Gaussian distribution. If a polyp exists in the WCE image, edges will be seen at the border of the occupied area. Since WCE images often suffer from low illumination, a histogram equalization (HE) technique can be used to enhance the image. In this paper, we initially find probable polyp edges via thresholding. Then, we use the edges to find the region of interest (ROI). Then, the mean, standard deviation (STD), and division of mean by STD from the ROI are computed as features to discriminate between polyp and nonpolyp using a support vector machine (SVM). The evaluation results on the Kvasir-Capsule dataset show 99% accuracy for the proposed method in polyp detection. Furthermore, the proposed method runs at a real-time speed of ∼0.031 seconds detection for each image.

Video Analysis of Small Bowel Capsule Endoscopy Using a Transformer Network.

Although wireless capsule endoscopy (WCE) detects small bowel diseases effectively, it has some limitations. For example, the reading process can be time consuming due to the numerous images generated per case and the lesion detection accuracy may rely on the operators' skills and experiences. Hence, many researchers have recently developed deep-learning-based methods to address these limitations. However, they tend to select only a portion of the images from a given WCE video and analyze each image individually. In this study, we note that more information can be extracted from the unused frames and the temporal relations of sequential frames. Specifically, to increase the accuracy of lesion detection without depending on experts' frame selection skills, we suggest using whole video frames as the input to the deep learning system. Thus, we propose a new Transformer-architecture-based neural encoder that takes the entire video as the input, exploiting the power of the Transformer architecture to extract long-term global correlation within and between the input frames. Subsequently, we can capture the temporal context of the input frames and the attentional features within a frame. Tests on benchmark datasets of four WCE videos showed 95.1% sensitivity and 83.4% specificity. These results may significantly advance automated lesion detection techniques for WCE images.

AUTOMATIC POLYP SEMANTIC SEGMENTATION USING WIRELESS CAPSULE ENDOSCOPY IMAGES WITH VARIOUS CONVOLUTIONAL NEURAL NETWORK AND OPTIMIZATION TECHNIQUES: A COMPARISON AND PERFORMANCE EVALUATION

Colorectal cancer (CRC), ranking third most prevalent cancer type, can be diagnosed with the detection of polyps in the colon and rectum through endoscopic procedures facilitating prompt treatment. During visualization of gastrointestinal tract by the physician, there is high probability of miss rates and reviewing of the images is laborious. Automatic segmentation and detection are enabled with the convolutional neural networks (CNN). We segmented the polyps from the wireless capsule endoscopy images of Kvasir dataset using various CNN models. We have presented nine optimizers for each architecture and evaluated the performance parameters. The optimizers were graded based on the performance metrics in order to provide an insight for the researchers on the selection of optimizer and architecture. On comparison of the performance metrics of the pretrained and U-net-based architecture, the Adaptive Moment Estimation (ADAM) and Root Mean Squared Propagation (RMSPROP) optimizers received the highest score of 43 in the ranking, DiffGrad and Nesterov-accelerated Adaptive Moment Estimation (NADAM) ranked second with the score of 13, the Adaptive Delta (ADADELTA) ranked third with a score of 2, whereas Stochastic Gradient Descent (SGD), Adaptive Gradient Descent (ADAGRAD), and Adaptive Max (ADAMAX) optimizers performed least in the evaluation. Based on the deep learning application, the optimizer employed varies by considering computational speed, memory and computational time. This preliminary research provides the necessary key information for consideration in the development of an architecture with utilization of an optimizer.

Emerging Trends and Advances in the Diagnosis of Gastrointestinal Diseases

Recently, Artificial Intelligence (AI)-based techniques, namely machine learning (ML) and deep learning (DL) have gained exceptional devotion in conducting the analysis of medical images because of their capacity to provide outstanding results that can compete with specialists. Despite the rise of artificial intelligence-based research on peptic ulcer diseases, limited reviews are available concerning this area. For this purpose, the researcher reviewed artificial intelligence techniques used for detecting and classifying gastrointestinal diseases in wireless capsule endoscopy images. Furthermore, this study investigates the tremendous potential for peptic ulcer disease that has been cited in the prior literature. The findings demonstrated the value of WCE picture analysis using machine learning and deep learning techniques. Additionally, further, limitations were found in the availability of datasets and assessment measures, which have an impact on the reproducibility of experiments.

A Convolutional Neural Network with Meta-feature Learning for Wireless Capsule Endoscopy Image Classification

A Convolutional Neural Network with Meta-feature Learning for Wireless Capsule Endoscopy Image Classification

Self-supervised out-of-distribution detection in wireless capsule endoscopy images

While deep learning has displayed excellent performance in a broad spectrum of application areas, neural networks still struggle to recognize what they have not seen, i.e., out-of-distribution (OOD) inputs. In the medical field, building robust models that are able to detect OOD images is highly critical, as these rare images could show diseases or anomalies that should be detected. In this study, we use wireless capsule endoscopy (WCE) images to present a novel patch-based self-supervised approach comprising three stages. First, we train a triplet network to learn vector representations of WCE image patches. Second, we cluster the patch embeddings to group patches in terms of visual similarity. Third, we use the cluster assignments as pseudolabels to train a patch classifier and use the Out-of-Distribution Detector for Neural Networks (ODIN) for OOD detection. The system has been tested on the Kvasir-capsule, a publicly released WCE dataset. Empirical results show an OOD detection improvement compared to baseline methods. Our method can detect unseen pathologies and anomalies such as lymphangiectasia, foreign bodies and blood with AUROC>0.6. This work presents an effective solution for OOD detection models without needing labeled images.

Enhancing Gastrointestinal Disease Diagnosis with KNN: A Study on WCE Image Classification

This study explores the application of the K-Nearest Neighbors (KNN) algorithm, following Sobel segmentation and Hu Moment feature extraction, to classify Wireless Capsule Endoscopy (WCE) images into Normal and Ulcerative Colitis conditions. Through a rigorous 5-fold cross-validation approach, the research aimed to determine the KNN algorithm's accuracy, precision, recall, and F1-score on the WCE Curated Colon Disease Dataset. The findings revealed high performance across all metrics, with accuracy rates extending up to 90.625%. The confusion matrix provided further validation, illustrating a high true positive rate coupled with a low false negative rate. These results substantiate the hypothesis that employing edge detection and shape descriptors as pre-processing techniques can significantly enhance the efficacy of machine learning algorithms in medical image classification. The study’s contribution is twofold: it reaffirms the potential of machine learning in the advancement of medical diagnostics and provides a methodological framework for automated image classification that can assist clinicians. It is recommended that future research extends to broader datasets and explores various algorithms to enhance diagnostic precision. In practice, integrating this research into a clinical decision support system could revolutionize diagnostic processes, offering a non-invasive, accurate, and efficient tool for gastroenterological diagnostics.

Combining deep features and hand-crafted features for abnormality detection in WCE images

In this paper, a computer-aided method is proposed for abnormality detection in Wireless Capsule Endoscopy (WCE) video frames. Common abnormalities in WCE images include ulcers, bleeding, Angiodysplasia, Lymphoid Hyperplasia, and polyp. In this paper, deep features and Hand-crafted features are combined to detect these abnormalities in WCE images. There are not sufficient images available to train deep structures, therefore the ResNet50 pre-trained model is used to extract deep features. Hand-crafted features are associated with color, shape, and texture. We used a novel idea to reveal unexpected color changes in the background due to existing lesions as a color feature set. Histogram of gradient (HOG) and local binary pattern (LBP) were used respectively for shape and texture features. They are extracted from the region of interest (ROI), i.e. suspicious region. The expectation Maximization (EM) algorithm is used to extract more distinct areas in the background as ROI. The expectation Maximization (EM) algorithm is configured in a way that can extract areas with a distinct texture and color as ROI. The EM algorithm is also initialized with a new fast method which leads to an increase in the accuracy of the method. A large number of features are created by the method, so the minimum redundancy maximum relevance approach is used to select a subset of more effective features. These selected features are then fed to a Support Vector Machine for classification. The results show that the proposed approach can detect mentioned abnormalities in WCE frames with the accuracy of 97.82%

Effective deep learning based segmentation and classification in wireless capsule endoscopy images

Effective deep learning based segmentation and classification in wireless capsule endoscopy images

Automated Wireless Capsule Endoscopy Image Classification using Reptile Search Optimization with Deep Learning Model

Automated Wireless Capsule Endoscopy Image Classification using Reptile Search Optimization with Deep Learning Model

Efficient Gastrointestinal Disease Classification Using Pretrained Deep Convolutional Neural Network

Gastrointestinal (GI) tract diseases are on the rise in the world. These diseases can have fatal consequences if not diagnosed in the initial stages. WCE (wireless capsule endoscopy) is the advanced technology used to inspect gastrointestinal diseases such as ulcerative-colitis, polyps, esophagitis, and ulcers. WCE produces thousands of frames for a single patient’s procedure for which manual examination is tiresome, time-consuming, and prone to error; therefore, an automated procedure is needed. WCE images suffer from low contrast which increases inter-class and intra-class similarity and reduces the anticipated performance. In this paper, an efficient GI tract disease classification technique is proposed which utilizes an optimized brightness-controlled contrast-enhancement method to improve the contrast of the WCE images. The proposed technique applies a genetic algorithm (GA) for adjusting the values of contrast and brightness within an image by modifying the fitness function, which improves the overall quality of WCE images. This quality improvement is reported using qualitative measures, such as peak signal to noise ratio (PSNR), mean square error (MSE), visual information fidelity (VIF), similarity index (SI), and information quality index (IQI). As a second step, data augmentation is performed on WCE images by applying multiple transformations, and then, transfer learning is used to fine-tune a modified pre-trained model on WCE images. Finally, for the classification of GI tract disease, the extracted features are passed through multiple machine-learning classifiers. To show the efficacy of the proposed technique in the improvement in classification performance, the results are reported for the original dataset as well as the contrast-enhanced dataset. The results show an overall improvement of 15.26% in accuracy, 13.3% in precision, 16.77% in recall rate, and 15.18% in F-measure. Finally, a comparison with the existing techniques shows that the proposed framework outperforms the state-of-the-art techniques.

English

Wireless capsule endoscopy (WCE) is a non-invasive wireless imaging technology that gained wider popularity. The main drawback of WCE is that it produces a massive amount of images that healthcare professionals should analyze, which is time-consuming. Many researchers have suggested machine learning and image-processing methods for classifying gastrointestinal tract disorders. Data augmentation and classical image processing techniques are integrated with the adjustable pre-trained deep convolutional neural network (DCNN) to categorize diseases in the digestive tract from WCE images. This study develops an Intelligent Wireless Endoscopic Image Classification using Gannet Optimization Algorithm with Deep Learning (IWEIC-GOADL) model. The IWEIC-GOADL technique mainly examines the WCE images for classification purposes. As a preprocessing step, the presented IWEIC-GOADL technique executes the Gabor filtering (GF) method for the noise removal process. In addition, the presented IWEIC-GOADL technique employs a deconvolution VGG19 (DeVGG19) model for feature vector generation, and its hyperparameter tuning process takes place by the GOA. Finally, the IWEIC-GODL technique applies the deep belief network (DBN) model for WCE image classification purposes. A wide range of simulations was performed on a benchmark dataset to demonstrate the better performance of the IWEIC-GODL technique. The stimulation outcome stated the improvements of the IWEIC-GODL algorithm over other recent techniques.

Classification of endoscopic image and video frames using distance metric-based learning with interpolated latent features.

The online version contains supplementary material available at 10.1007/s11042-023-14982-1.