732 Results Found

Several studies in computer vision have examined specular removal, which is crucial for object detection and recognition. This research has traditionally been divided into two tasks: specular highlight removal, which focuses on removing specular high...

Image segmentation plays a central role in a broad range of applications, such as medical image analysis, autonomous vehicles, video surveillance and augmented reality. Portrait segmentation, which is a subset of semantic image segmentation, is widel...

This paper focused on the problem of diagnosis of Alzheimer’s disease via the combination of deep learning and radiomics methods. We proposed a classification model for Alzheimer’s disease diagnosis based on improved convolution neural network models...

Low-light image enhancement is very significant for vision tasks. We introduce Low-light Image Enhancement via Deep Learning Network (LLE-NET), which employs a deep network to estimate curve parameters. Cubic curves and gamma correction are employed...

Background/Objectives: To develop and validate deep learning-based models for detecting significant steno-occlusion (SSO)—defined as luminal narrowing greater than 50%—of the femoropopliteal arteries using maximum intensity projection (MI...

Classifying brain tumour transcriptomic data is crucial for precision medicine but remains challenging due to high dimensionality and limited interpretability of conventional models. This study benchmarks three image-based deep learning approaches, D...

The World Health Organization and Korea National Health Insurance assert that the number of alopecia patients is increasing every year, and approximately 70 percent of adults suffer from scalp problems. Although alopecia is a genetic problem, it is d...

The goal of single image super resolution (SISR) is to recover a high-resolution (HR) image from a low-resolution (LR) image. Deep learning based methods have recently made a remarkable performance gain in terms of both the effectiveness and efficien...

In the study of the deep learning classification of medical images, deep learning models are applied to analyze images, aiming to achieve the goals of assisting diagnosis and preoperative assessment. Currently, most research classifies and predicts n...

Background: Video fluoroscopic swallowing study (VFSS) is considered as the gold standard diagnostic tool for evaluating dysphagia. However, it is time consuming and labor intensive for the clinician to manually search the recorded long video image f...

Computed tomography (CT) images are currently being adopted as the visual evidence for COVID-19 diagnosis in clinical practice. Automated detection of COVID-19 infection from CT images based on deep models is important for faster examination. Unfortu...

Objectives: To propose a multimodal deep learning method for the classification of benign and malignant breast non-mass lesions (NMLs) using grayscale and color Doppler ultrasound and to compare the performance of multi-modality and single-modality b...

The combination of multi-temporal images and deep learning is an efficient way to obtain accurate crop distributions and so has drawn increasing attention. However, few studies have compared deep learning models with different architectures, so it re...

In the past few years, deep learning has gained increasingly widespread attention and has been applied to diagnosing benign and malignant thyroid nodules. It is difficult to acquire sufficient medical images, resulting in insufficient data, which hin...

An end-to-end image compression framework based on deep residual learning is proposed. Three levels of residual learning are adopted to improve the compression quality: (1) the ResNet structure; (2) the deep channel residual learning for quantization...

The fixed-pattern noise (FPN) caused by nonuniform optoelectronic response limits the sensitivity of an infrared imaging system and severely reduces the image quality. Therefore, nonuniform correction of infrared images is very important. In this pap...

Monitoring calf body weight (BW) before weaning is essential for assessing growth, feed efficiency, health, and weaning readiness. However, labor, time, and facility constraints limit BW collection. Additionally, Holstein calf coat patterns complicat...

Multiplexed positron emission tomography (PET) imaging provides perfectly registered simultaneous functional and molecular imaging of more than one biomarker. However, the separation of the multiplexed PET signals within a single PET scan is challeng...

Predicting subcellular protein localization has become a popular topic due to its utility in understanding disease mechanisms and developing innovative drugs. With the rapid advancement of automated microscopic imaging technology, approaches using bi...

Online learning is a method for exploiting input data to update deep networks in the test stage to derive potential performance improvement. Existing online learning methods for single-image super-resolution (SISR) utilize an input low-resolution (LR...

Screening programs for early lung cancer diagnosis are uncommon, primarily due to the challenge of reaching at-risk patients located in rural areas far from medical facilities. To overcome this obstacle, a comprehensive approach is needed that combin...

In medical image retrieval, accurately retrieving relevant images significantly impacts clinical decision making and diagnostics. Traditional image-retrieval systems primarily rely on single-dimensional image data, while current deep-hashing methods...

Research on image classification sparked the latest deep-learning boom. Many downstream tasks, including semantic segmentation, benefit from it. The state-of-the-art semantic segmentation models are all based on deep learning, and they sometimes make...

Background: This study aimed to evaluate the potential of human–machine interaction (HMI) in a deep learning software for discerning the malignancy of choroidal melanocytic lesions based on fundus photographs. Methods: The study enrolled indivi...

This study uses hyperspectral imaging (HSI) and a deep learning diagnosis model that can identify the stage of esophageal cancer and mark the locations. This model simulates the spectrum data from the image using an algorithm developed in this study...

Airplane detection in remote sensing images remains a challenging problem due to the complexity of backgrounds. In recent years, with the development of deep learning, object detection has also obtained great breakthroughs. For object detection tasks...

To noninvasively and precisely discriminate among the growth stages of multiple cultivars of navel oranges simultaneously, the fusion of the technologies of near-infrared (NIR) hyperspectral imaging (HSI) combined with machine vision (MV) and deep le...

Training a deep learning-based classification model for early wildfire smoke images requires a large amount of rich data. However, due to the episodic nature of fire events, it is difficult to obtain wildfire smoke image data, and most of the samples...

Although many efforts have been made through past years, skin cancer recognition from medical images is still an active area of research aiming at more accurate results. Many efforts have been made in recent years based on deep learning neural networ...

The haze in remote sensing images can cause the decline of image quality and bring many obstacles to the applications of remote sensing images. Considering the non-uniform distribution of haze in remote sensing images, we propose a single remote sens...

Owing to recent advancements in deep learning methods and relevant databases, it is becoming increasingly easier to recognize 3D objects using only RGB images from single viewpoints. This study investigates the major breakthroughs and current progres...

Single Image Super Resolution (SSIR) is an intriguing research topic in computer vision where the goal is to create high-resolution images from low-resolution ones using innovative techniques. SSIR has numerous applications in fields such as medical/...

High-content and high-throughput digital microscopes have generated large image sets in biological experiments and clinical practice. Automatic image analysis techniques, such as cell counting, are in high demand. Here, cell counting was treated as a...

Intelligent mobile image sensing powered by deep learning analyzes images captured by cameras from mobile devices, such as smartphones or smartwatches. It supports numerous mobile applications, such as image classification, face recognition, and came...

Pediatric medulloblastomas (MBs) are the most common type of malignant brain tumors in children. They are among the most aggressive types of tumors due to their potential for metastasis. Although this disease was initially considered a single disease...

The annotation of magnetic resonance imaging (MRI) images plays an important role in deep learning-based MRI segmentation tasks. Semi-automatic annotation algorithms are helpful for improving the efficiency and reducing the difficulty of MRI image an...

Focusing on coronary artery disease (CAD) patients, this research paper addresses the problem of automatic diagnosis of ischemia or infarction using single-photon emission computed tomography (SPECT) (Siemens Symbia S Series) myocardial perfusion ima...

Background: In the context of rapid urbanization, the need for building safety and durability assessment is becoming increasingly prominent. Objective: The aim of this paper is to review the strengths and weaknesses of the main non-destructive testin...

Deep learning models have shown great potential in scientific research, particularly in remote sensing for monitoring natural resources, environmental changes, land cover, and land use. Deep semantic segmentation techniques enable land cover classifi...

It is significant to identify rock-mineral microscopic images in geological engineering. The task of microscopic mineral image identification, which is often conducted in the lab, is tedious and time-consuming. Deep learning and convolutional neural...

In this paper, we propose a deep learning model based on dual-branch learning with a hybrid attention mechanism for meeting challenges in the underutilization of features in ophthalmic image diagnosis and the limited generalization ability of traditi...

Deep-learning (DL)-based methods are of growing importance in the field of single image super-resolution (SISR). The practical application of these DL-based models is a remaining problem due to the requirement of heavy computation and huge storage re...

In the realm of solar photovoltaic system image segmentation, existing deep learning networks focus almost exclusively on single image sources both in terms of sensors used and image resolution. This often prevents the wide deployment of such network...

Despite their proficiency with typical environmental datasets, deep learning-based object detection algorithms struggle when faced with diverse adverse weather conditions. Moreover, existing methods often address single adverse weather scenarios, neg...

Cardiothoracic surgery patients have the risk of developing surgical site infections which cause hospital readmissions, increase healthcare costs, and may lead to mortality. This work aims to tackle the problem of surgical site infections by predicti...

The identification of minerals is indispensable in geological analysis. Traditional mineral identification methods are highly dependent on professional knowledge and specialized equipment which often consume a lot of labor. To solve this problem, som...

Traditional methods for individual tree-crown (ITC) detection (image classification, segmentation, template matching, etc.) applied to very high-resolution remote sensing imagery have been shown to struggle in disparate landscape types or image resol...

In recent years, underwater imaging and vision technologies have received widespread attention, and the removal of the backward-scattering interference caused by impurities in the water has become a long-term research focus for scholars. With the adv...

Underwater imaging presents unique challenges, notably color distortions and reduced contrast due to light attenuation and scattering. Most underwater image enhancement methods first use linear transformations for color compensation and then enhance...

Remote sensing images captured by satellites play a critical role in Earth observation (EO). With the advancement of satellite technology, the number and variety of remote sensing satellites have increased, which provide abundant data for precise env...