A revolutionary imaging device termed wireless capsule endoscopy (WCE) provides a painless, noninvasive vision of the complete gastrointestinal (GI) tract. Given the poor intensities and vague differences between bleeding and normal regions, successful bleeding region detection in WCE recordings is essential for the premature diagnosis and restoration of intestinal cancer. Conversely, in this research, we propose a flexible segmentation map construction technique centered on U-shaped architecture and supervised by the suggested Separa-ble Contextual Feature-Guided (SCFG) module to identify GI bleeding. The SCFG module arrives at every encoder-decoder block for guiding the model in accurately recognizing various bleeding zones. The primary drawback is that the module can be improved by better characterizing the bleeding zone due to the usage of numerous alternative convolutional forms. Additionally, a bottleneck module is proposed, serving as a feature transition module. The results of the studies reveal that our suggested system outperforms state-of-the-art segmentation techniques by a margin of 0.47 % in the dice coefficient. The dice score's calculated segmentation rate is 91.14 %, while the intersection over union (IoU) segmentation efficiency is 83.72 %.

The human gastrointestinal (GI) tract is sus-ceptible to a myriad of diseases that can profoundly impact health. Therefore, timely detection and intervention are critical in halting the progression of these diseases and preventing their potential transformation into cancer. Regular screenings, diagnostic tests, and early symptom recognition play vital roles in ensuring early intervention and better patient outcomes in managing GI-related conditions. In recent years, scientists have increasingly turned to advanced technologies, particularly deep learning algorithms, to revolutionize the detection and segmentation of colorectal anomalies. Leveraging the power of artificial intelligence, researchers are exploring sophisticated deep learning models capable of analyzing vast amounts of endoscopic imagery with remarkable precision and efficiency. In our proposed paper, we introduce an approach to the automated segmentation of colorectal anomalies, through the development of an end-to-end architecture named VGGI6U-Net. The architecture enhances the capability of the framework for precise segmentation By leveraging the features of VGG 16 and integrating an attention mechanism into the U-Net framework. The model exhibits promising performance in accurately identifying and delineating polyps and bleeding regions within images. The incorporation of the attention mechanism enables the network to focus on salient features, thereby further improving segmentation accuracy and reducing false positives.

Globally, colorectal cancer is the primary cause of cancer-related death. Colonoscopy is currently one of the most common ways to identify precancerous gastrointestinal disorders. Thus, early and precise polyp segmentation is of therapeutic importance in reducing the risk of developing cancer. The manual examination is a tedious and time-consuming procedure for physicians. Many computer algorithms have been created by scientists to automatically identify problems from colonoscopic images. In order to further increase polyp segmentation performance, we provide in this study an attDenseU-Net design that concurrently includes the attention mechanism and U-Net. AttDenseU-Net reduces the amount of background in an input image while emphasizing key components by inserting a sequence of attention units in between relevant downs amp ling and upsampling operations. Integrating DenseNet blocks into U-Net architecture helps to more effective and efficient feature learning, and improved gradient flow. A publicly available dataset called K vasir-SEG was utilized in this study to evaluate and confirm the proposed approach. Our model's accuracy rate is 86.31 %, its Dice coefficient is 91.48%, and its Jaccard index is 84.30 %. The experiment findings show that the proposed AttDenseU-Net outperforms its baselines and provide s performance on par with existing polyp segmentation methods.

Super-resolution aims to enhance the quality of a low-resolution image to create a high-resolution one. Remarkable advances are witnessed in this field using machine learning techniques . This paper presents a super-resolution method based on generative adversarial networks (GAN) with quantum feature enhancement. The proposed framework uses a feature enhancement layer inspired by the quantum superposition principle . The layer was added to the state-of-the art super-resolution GAN (SRGAN) original model to enhance its performance. The model was trained and evaluated using two publicly available high-resolution aerial images datasets taken by an unmanned aerial vehicle . A set of statistically significant experiments are reported to show its performance. The structural similarity index metric (SSIM), t-distributed stochastic neighbor embedding (t-SNE) and peak signal-to-noise ratio (PSNR) are adopted to evaluate the performance of this proposal against SRGAN model. Results show that this proposal outperforms SRGAN in term of image reconstruction quality by 8% in similarity.

This study leverages advancements in deep learning (DL) to enhance the analysis of satellite image time series (SITSs) in marine geoscience, focusing on the prediction of sea surface temperature (SST) and the detection of coastal upwelling. By employing convolutional neural networks (CNNs) and recurrent neural networks (RNNs), including long short-term memory (LSTM) networks, we introduce a novel approach utilizing convolutional LSTM (ConvLSTM) and 3-D Unet-LSTM models. These techniques provide a nuanced analysis and understanding of complex oceanographic phenomena, specifically coastal upwelling, which significantly impacts marine ecosystems and climate. The adoption of these sophisticated DL models has led to a notable improvement in predicting SST fields, achieving a reduction in root mean square error (RMSE) to 0.038 and an increase in the correlation coefficient (CC) to 0.95. This enhancement over the baseline ConvLSTM model, which had an RMSE of 0.045 and a CC of 0.92, underscores our models’ capability to accurately capture the dynamic and intricate nature of coastal upwelling. The results offer promising directions for future research in marine geoscience and remote-sensing applications, highlighting the potential of DL techniques in interpreting intricate patterns in satellite-derived data and improving predictions in environmental sciences.

Colorectal cancer is the main reason for mortality from cancer globally. One of the most popular methods for spotting precancerous gut illnesses right now is a colonoscopy. As a result, early and accurate polyp segmentation has great therapeutic significance in lowering the likelihood that cancer may develop. For doctors, the manual examination is a laborious and time-consuming process. In order to automatically separate the abnormalities from endoscopic pictures, numerous computer algorithms have been developed by scientists. In this research, we introduce an attention U-Net architecture that simultaneously incorporates the attention mechanism and U-Net for further performance improvement of polyp segmentation. AttResU-Net inserts a series of attention units between related downsampling and upsampling processes in order to minimize outside regions in an input image while accentuating important elements. This paper used CVC-ClinicDB, a publicly accessible dataset to assess and validate the suggested strategy. Our model has a 97.46% accuracy rate, a 90.85% Dice coefficient, and a 83.26% Jaccard index. The results of the trial demonstrate that the suggested AttentionU-Net surpasses its baselines and offers performance comparable to current polyp segmentation techniques.

Vision Transformer (ViT) and its variants have gained significant prominence in computer vision due to their exceptional performance across various tasks. However, ViTs are data-hungry models that require vast amounts of data for training. Given the scarcity of medical data, this paper presents a fine-tuned vision transformer specifically designed for small-size datasets. We fine-tuned the original model using convolutional neural networks (CNNs) to extract both high and low-level features from wireless capsule endoscopy (WCE) images. In this work, we integrate a CNN module into the original ViT to extract features from WCE patches, which are then fused with the original ViT features. The classification is accomplished using a multilayer perceptron (MLP) to categorize images into normal and abnormal categories on the Kvasir Capsule Endoscopy dataset, as well as bleeding or non-bleeding categories on the Red Lesion Endoscopy dataset. The experimental findings substantiate the efficacy of the suggested approach, yielding favorable outcomes in comparison to other state-of-the-art methods.

Wireless capsule endoscopy (WCE) is a technology that captures images of the digestive tract with a pill-sized camera. Capsule endoscopies are used to exclude or diagnose disorders such as bleeding, early symptoms of gastrointestinal cancer, abdominal pain, Crohn's disease, Celiac disease, polyps, and ulcers. However, the main cause for a capsule endoscopy is to scout for small intestine haemorrhage. Because of the technological limits, the images are low quality and feature multiple orientations due to the capsule's free mobility. In this study, we propose a technique for detecting bleeding in WCE images. We deploy a deep neural network that uses the Inception-ResNet-V2 model for its high level, combined with a low-level model that is a convolutional neural network (CNN), to attain better classification performance. The proposed methods' average accuracy is 98.5 %, with sensitivity, specificity, and precision of 98.5 %, 99 %, and 98.5 %, respectively. It clearly shows that our method outperforms state-of-the-art approaches in detecting haemorrhage.

Recently, pedestrian detection has attracted a lot of attention in recent years. It is known as a computer vision research hotspot, widely used in different fields. Despite the impressive progress of its approaches, their performance remains unsatisfactory. This paper proposes PedVis-Vgg-16a deep learning network for automatically detecting pedestrians presence in visible images. The suggested architecture is based on the fine-tuned VGG-16 architecture with modifications to the last block of the model. Different improvement components including data augmentation, parameter optimization, and parameter adaption, were taken to enhance the architecture performance. The newly designed architecture is validated on the publicly available dataset INRIA, which contains 4001 images and the results provided are satisfactory.

Wireless capsule endoscopy (WCE) is a non-surgical diagnostic procedure enabling the examination of the whole human gastrointestinal tract. Thus, a patient swallows a capsule that travels down the human digestive system and a camera captures wirelessly thousands of images that are transmitted to an external recording device. The diagnosis of these images need a specialist who can identify gastrointestinal abnormalities and it is very time-consuming. Recently, artificial intelligence and deep learning techniques aim to automate disease diagnosis and identi-fication of tumors in the gastrointestinal tract (GI) such as polyps, ulcers and bleeding, etc. In this paper, a deep learning method is proposed for gastrointestinal disease classification. The pre-trained model ResNetSO is fine-tuned through transfer learning to extract deep features from WCE images. The proposed algorithm is trained and tested on the publicly available dataset k-vasir capsule, which contains 14 different classes of gastrointestinal anomalies.

Making information stored in database accessible for non expert users, has become one of the problems of great interest for the research community of database querying system. Hence for overriding the complexity of using database language such as Structured Query Language (SQL), the using of natural language can be a very important and simple method. But without helps computer cannot understand this language. For that its necessary to develop an interface able to translate natural language query into database query language. In this paper we present the architecture of generic interface for querying database using Arabic language. This interface functions independently of database domain and has the capacity to improve through experience its knowledge base.

In the omnidirectional vision, the catadioptric sensors causes a non-uniform resolution and geometric distortions in resulting images, hence, the conventional processing methods are not convenient. The aim of this work is to adapt matching method based on the proximity criterion to omnidirectional stereovision. The adapted method based on geodesic distance since the treatments are done on a spherical space. The similarity measures used are mutual information and correlation coefficient. We chose the adapted processing methods rather than conventional ones. We work in the case which the sensor has a slight displacement between two images. The tests are done on normal and noisy images. The results are satisfactory.

In this paper, we present a fast approach for matching stereo images acquired by a stereo sensor embedded in a moving vehicle. The proposed approach exploits the disparity map already computed at the preceding frame to improve the matching results at the current one. An edge association method is used to track the edge curves over time. Local disparity constraints are computed for all the edge points that belong to the tracked edge curves. For the rest of edge points, we use a global disparity constraint, which is computed for each image line based on the preceding v-disparity. We integrate these constraints in the dynamic programming algorithm, which increases the matching results and speeds up the matching process.