Sensor Data Fusion Based on Deep Learning for Computer Vision Applications and Medical Applications

Arsalan, Muhammad; Qaiser, Talha; Khan, Tariq M.; Razzak, Imran

doi:10.3390/s22208058

Cited by 6 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is due to the fact that the combination of different sort of sensors and different measuring strategies taken in a convenient fashion improves the accuracy and robustness of the outcome obtained by each sensor on an individual basis [53]. In this sense, sensor data fusion enhances reliability, range and accuracy of measurements in order to boost performance rates [54]. One possible strategy if three fusion layers are implemented is the following: the first one converts raw data obtained by sensors into a logical value, whereas the second one establishes a fusion tree and the values of intermediate nodes are calculate according to predefined logical operations, while the third one makes the final decision by accounting for the value of the root, which is given by means of predetermined equations [55].…”

Section: Fault Diagnosis and Detectionmentioning

confidence: 99%

Study on Data Center Network Topologies for Monitoring Data using Edge/Fog Computing

Roig,

Alcaraz,

Gılly

et al. 2024

Politeknik Dergisi

View full text Add to dashboard Cite

The election of an appropriate data center network topology is key when dealing with surveillance and monitoring processes, such as those devoted to obtaining relevant data for sensor data fusion in any type of remote computing environment so as to perform fault diagnosis and prevention. In this paper, some of the most commonly used topologies to interconnect nodes within a data center bound to edge/fog computing, representing either switch-centric ones or server-centric ones, are reviewed and analyzed from a statistical point of view in order to measure their performance, resulting in server-centric ones doing it better.

show abstract

Section: Fault Diagnosis and Detectionmentioning

confidence: 99%

Study on Data Center Network Topologies for Monitoring Data using Edge/Fog Computing

Roig,

Alcaraz,

Gılly

et al. 2024

Politeknik Dergisi

View full text Add to dashboard Cite

show abstract

“…In recent years, with the ease of accessing data and the development of computers with faster processing power, artificial intelligence (AI) technologies have advanced, and expert systems have evolved into data-oriented AI applications. In particular, the increase in studies on the successful performance of deep learning methods, especially in image-based diagnostic tasks where a diagnosis is challenging, such as cancer [4], lung, and eye diseases [5,6], has increased interest in the medical application of AI [7,8]. Recent literature reviews have acknowledged the success of expert systems based on deep learning methods that compete with the performance of experts in image-based dental diagnostic tasks, especially the research presented in this article.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review

et al. 2023

View full text Add to dashboard Cite

Deep learning and diagnostic applications in oral and dental health have received significant attention recently. In this review, studies applying deep learning to diagnose anomalies and diseases in dental image material were systematically compiled, and their datasets, methodologies, test processes, explainable artificial intelligence methods, and findings were analyzed. Tests and results in studies involving human-artificial intelligence comparisons are discussed in detail to draw attention to the clinical importance of deep learning. In addition, the review critically evaluates the literature to guide and further develop future studies in this field. An extensive literature search was conducted for the 2019–May 2023 range using the Medline (PubMed) and Google Scholar databases to identify eligible articles, and 101 studies were shortlisted, including applications for diagnosing dental anomalies (n = 22) and diseases (n = 79) using deep learning for classification, object detection, and segmentation tasks. According to the results, the most commonly used task type was classification (n = 51), the most commonly used dental image material was panoramic radiographs (n = 55), and the most frequently used performance metric was sensitivity/recall/true positive rate (n = 87) and accuracy (n = 69). Dataset sizes ranged from 60 to 12,179 images. Although deep learning algorithms are used as individual or at least individualized architectures, standardized architectures such as pre-trained CNNs, Faster R-CNN, YOLO, and U-Net have been used in most studies. Few studies have used the explainable AI method (n = 22) and applied tests comparing human and artificial intelligence (n = 21). Deep learning is promising for better diagnosis and treatment planning in dentistry based on the high-performance results reported by the studies. For all that, their safety should be demonstrated using a more reproducible and comparable methodology, including tests with information about their clinical applicability, by defining a standard set of tests and performance metrics.

show abstract

“…Two firms, from Sweden and Australia, working on IVF, have employed AI-based software to assist in embryo selection procedures; however, these are experimental setups and are in the pipeline for clinical validation [16]. Specifically, segmentation algorithms have substantially aided in the automatic analysis and quantification of various diseases [17]. AI has also helped address infertility by providing several automatic solutions [6,18].…”

Section: Introductionmentioning

confidence: 99%

Assisting the Human Embryo Viability Assessment by Deep Learning for In Vitro Fertilization

Ishaq¹,

Raza²,

Rehar³

et al. 2023

Mathematics

Self Cite

View full text Add to dashboard Cite

The increasing global infertility rate is a matter of significant concern. In vitro fertilization (IVF) significantly minimizes infertility by providing an alternative clinical means of becoming pregnant. The success of IVF mainly depends on the assessment and analysis of human blastocyst components such as the blastocoel (BC), zona pellucida (ZP), inner cell mass (ICM), and trophectoderm (TE). Embryologists perform a morphological assessment of the blastocyst components for the selection of potential embryos to be used in the IVF process. Manual assessment of blastocyst components is time-consuming, subjective, and prone to errors. Therefore, artificial intelligence (AI)-based methods are highly desirable for enhancing the success rate and efficiency of IVF. In this study, a novel feature-supplementation-based blastocyst segmentation network (FSBS-Net) has been developed to deliver higher segmentation accuracy for blastocyst components with less computational overhead compared with state-of-the-art methods. FSBS-Net uses an effective feature supplementation mechanism along with ascending channel convolutional blocks to accurately detect the pixels of the blastocyst components with minimal spatial loss. The proposed method was evaluated using an open database for human blastocyst component segmentation, and it outperformed state-of-the-art methods in terms of both segmentation accuracy and computational efficiency. FSBS-Net segmented the BC, ZP, ICM, TE, and background with intersections over union (IoU) values of 89.15, 85.80, 85.55, 80.17, and 95.61%, respectively. In addition, FSBS-Net achieved a mean IoU for all categories of 87.26% with only 2.01 million trainable parameters. The experimental results demonstrate that the proposed method could be very helpful in assisting embryologists in the morphological assessment of human blastocyst components.

show abstract

Sensor Data Fusion Based on Deep Learning for Computer Vision Applications and Medical Applications

Abstract: Sensor fusion is the process of merging data from many sources, such as radar, lidar and camera sensors, to provide less uncertain information compared to the information collected from single source [...]

Cited by 6 publications

References 10 publications

Study on Data Center Network Topologies for Monitoring Data using Edge/Fog Computing

Study on Data Center Network Topologies for Monitoring Data using Edge/Fog Computing

Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review

Assisting the Human Embryo Viability Assessment by Deep Learning for In Vitro Fertilization

Contact Info

Product

Resources

About