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Wireless capsule endoscopy (WCE) is capable of non-invasively visualizing the small intestine, the most complicated segment of the gastrointestinal tract, to detect different types of abnormalities. However, its main drawback is reviewing the vast number of captured images (more than 50,000 frames). The recorded images are only sometimes clear, and different contaminating agents, such as turbid materials and air bubbles, degrade the visualization quality of the WCE images. This condition could cause serious problems such as reducing mucosal view visualization, prolonging recorded video reviewing time, and increasing the risks of missing pathology. On the other hand, accurately quantifying the amount of turbid fluids and bubbles can indicate potential motility malfunction. To assist in developing computer vision-based techniques, we have constructed the first multicentre publicly available clear and contaminated annotated dataset by precisely segmenting 17,593 capsule endoscopy images from three different databases.In contrast to the existing datasets, our dataset has been annotated at the pixel level, discriminating the clear and contaminated regions and subsequently differentiating bubbles and turbid fluids from normal tissue. To create the dataset, we first selected all of the images (2906 frames) in the reduced mucosal view class covering different levels of contamination and randomly selected 12,237 images from the normal class of the copyright-free CC BY 4.0 licensed small bowel capsule endoscopy (SBCE) images from the Kvasir capsule endoscopy database. To mitigate the possible available bias in the mentioned dataset and to increase the sample size, the number of 2077 and 373 images have been stochastically chosen from the SEE-AI project and CECleanliness datasets respectively for the subsequent annotation. Randomly selected images have been annotated with the aid of ImageJ and ITK-SNAP software under the supervision of an expert SBCE reader with extensive experience in gastroenterology and endoscopy. For each image, two binary and tri-colour ground truth (GT) masks have been created in which each pixel has been indexed into two classes (clear and contaminated) and three classes (bubble, turbid fluids, and normal), respectively.To the best of the author's knowledge, there is no implemented clear and contaminated region segmentation on the capsule endoscopy reading software. Curated multicentre dataset can be utilized to implement applicable segmentation algorithms for identification of clear and contaminated regions and discrimination bubbles, as well as turbid fluids from normal tissue in the small intestine.Since the annotated images belong to three different sources, they provide a diverse representation of the clear and contaminated patterns in the WCE images. This diversity is valuable for training the models that are more robust to variations in data characteristics and can generalize well across different subjects and settings. The inclusion of images from three different centres allows for robust cross-validation opportunities, where computer vision-based models can be trained on one centre's annotated images and evaluated on others.

Abstract Cardiac failure, such as myocardial infarction (MI), is one of the most serious causes of mortality worldwide. MI is the sign of cardiac cell damage as a result of decreased blood oxygen level, which causes some morphological changes in the form of 12‐lead electrocardiogram (ECG) waves including T‐wave, Q‐wave, and ST‐segment. The main goal of this study is to represent vectorcardiography (VCG) as a complementary diagnostic tool of the ECG method to discriminate the various type of MI from normal cases. The system proposed in this study was analysed on the Physikalisch‐Technische Bundesanstalt diagnostic ECG database and a recorded signal database for 80 MI and 52 healthy cases. Each record consists of 15 ECG and VCG signals. In this study, tridimensional morphological features were applied to the classification and regression tree (CART) and the feedforward neural network classifier. To classify MI cases from healthy control cases of our recorded database, classification and regression tree achieved the same results when VCG features or ECG features were applied with an accuracy of 99.4%, a sensitivity of 100%, and a specificity of 98.7%. Further, by using VCG Octant features with this current method, anterior‐MI and inferior‐MI were separated with an accuracy of 98.9%, a sensitivity of 98%, and a specificity of 100%. The outcomes prove that the VCG features performed more accurately than ECG features in MI localisation.

Objective: A large number of captured frames by the wireless capsule endoscopy have been contaminated with different amounts of bubbles. Bubbles can degrade the visualization quality of the small intestine mucosa. The aim of this study is to develop an objective method for evaluating the amount of bubbles in WCE images. Methods: Frames with varying levels of bubble occlusion were selected from the Kvasir capsule endoscopy dataset. The round shape bubbles have an edge in their boundaries. Edges in the spatial domain correspond to high-frequency bands in the frequency domain. Two automated edge detection approaches have been developed in a rule-based manner and evaluated to assess the amount of bubbles. The first approach involved high pass filtering using fast Fourier transform (FFT), while the second approach has been based on wavelet image decomposition and reconstruction by omitting approximation coefficients subband. Results: Both Fourier and wavelet transforms obtained approximately the same dice similarity score (DSC), and precision metrics, which were equal to 0.87, and 0.91, respectively. Based on the specificity measure, the FFT outperformed the Hough and wavelet transforms. However, the wavelet transform obtained a higher dice similarity score (DSC) (0.93), accuracy (0.95), and sensitivity metric (0.97) and was the fastest, with an execution time of 0.01 s per frame, making it suitable for real-time applications. Conclusion: The proposed technique provides an easy-to-implement method for quality reporting or objective comparison tools of different bowel preparation paradigms in real-time applications due to its fast execution time. The obtained results from two different datasets proved that the presented method has good generalization.

Background: Macular disorders, such as diabetic macular edema (DME) and age-related macular degeneration (AMD) are among the major ocular diseases. Having one of these diseases can lead to vision impairments or even permanent blindness in a not-so-long time span. So, the early diagnosis of these diseases are the main goals for researchers in the field. Methods: This study is designed in order to present a comparative analysis on the recent convolutional mixture of experts (CMoE) models for distinguishing normal macular OCT from DME and AMD. For this purpose, we considered three recent CMoE models called Mixture ensemble of convolutional neural networks (ME-CNN), Multi-scale Convolutional Mixture of Experts (MCME), and Wavelet-based Convolutional Mixture of Experts (WCME) models. For this research study, the models were evaluated on a database of three different macular OCT sets. Two first OCT sets were acquired by Heidelberg imaging systems consisting of 148 and 45 subjects respectively and set3 was constituted of 384 Bioptigen OCT acquisitions. To provide better performance insight into the CMoE ensembles, we extensively analyzed the models based on the 5-fold cross-validation method and various classification measures such as precision and average area under the ROC curve (AUC). Results: Experimental evaluations showed that the MCME and WCME outperformed the ME-CNN model and presented overall precisions of 98.14% and 96.06% for aligned OCTs respectively. For non-aligned retinal OCTs, these values were 93.95% and 95.56%. Conclusion: Based on the comparative analysis, although the MCME model outperformed the other CMoE models in the analysis of aligned retinal OCTs, the WCME offers a robust model for diagnosis of non-aligned retinal OCTs. This allows having a fast and robust computer-aided system in macular OCT imaging which does not rely on the routine computerized processes such as denoising, segmentation of retinal layers, and also retinal layers alignment.

In this study, we considered some competitive learning methods including hard competitive learning and soft competitive learning with/without fixed network dimensionality for reliability analysis in microarrays. In order to have a more extensive view, and keeping in mind that competitive learning methods aim at error minimization or entropy maximization (different kinds of function optimization), we decided to investigate the abilities of mixture decomposition schemes. Therefore, we assert that this study covers the algorithms based on function optimization with particular insistence on different competitive learning methods. The destination is finding the most powerful method according to a pre-specified criterion determined with numerical methods and matrix similarity measures. Furthermore, we should provide an indication showing the intrinsic ability of the dataset to form clusters before we apply a clustering algorithm. Therefore, we proposed Hopkins statistic as a method for finding the intrinsic ability of a data to be clustered. The results show the remarkable ability of Rayleigh mixture model in comparison with other methods in reliability analysis task.

Background: Up to date different methods have been used in order to dimensions reduction, classification, clustering and prediction of cancers based on gene expression profiling. The aim of this study is extracting most significant genes and classifying of Diffuse Large B-cell Lymphoma (DLBCL) patients on the basis of their gene expression profiles. Materials and Methods: We studied 40 DLBCL patients and 4026 genes. We utilized Artificial Neural Network (ANN) for classification of patients in two groups: Germinal center and Activated like. As we were faced with low number of patients (40) and numerous genes (4026), we tried to deploy one optimum network and achieve to minimum error. Moreover we used signal to noise (S/N) ratio as a main tool for dimension reduction. We tried to select suitable training data and so to train just one network instead of 26 networks. Finally, we extracted two most significant genes. Result: In this study two most significant genes based on their S/N ratios were selected. After selection of suitable training samples, the training and testing error were 0 and 7% respectively. Conclusion: We have shown that the use of two most significant genes based on their S/N ratios and selection of suitable training samples can lead to classify DLBCL patients with a rather good result. Actually with the aid of mentioned methods we could compensate lack of enough number of patients, improve accuracy of classifying and reduce complication of computations and so running time.