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The Degree of Skin Burns Images Recognition using Convolutional Neural Network
2016
Indian Journal of Science and Technology
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In recent years, Convolutional Neural Network (CNN) model is the stat of art model successful for image analysis. ...
The aim of this paper is to build to automated computer aided for identifying the degrees of burn images. ...
Convolution Neural Network (CNN) Apply for Skin Burn Images CNN model for skin burn images works as automatic skin burn wound recognition and computer aided in the burning victims diagnosis. system 4 . ...
doi:10.17485/ijst/2016/v9i45/106772
fatcat:fggd7pomzzavjait7z24zav4me
Front Matter: Volume 9785
2016
Medical Imaging 2016: Computer-Aided Diagnosis
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Publication of record for individual papers is online in the SPIE Digital Library. SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model. ...
These two-number sets start with 00, 01, 02, 03, 04, 05, 06, 07, 08, 09, 0A, 0B ... 0Z, followed by 10-1Z, 20-2Z, etc. The CID Number appears on each page of the manuscript. ...
network for computer-aided detection of
microcalcifications in digital breast tomosynthesis [9785-33]
9785 0Z
Computer aided lung cancer diagnosis with deep learning algorithms [9785-34]
9785 10 ...
doi:10.1117/12.2240961
dblp:conf/micad/X16
fatcat:b5addnksdrgp3ixwvbjt53xeqe
Front Matter: Volume 11597
2021
Medical Imaging 2021: Computer-Aided Diagnosis
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of lung cancer in screening vi Proc. of SPIE Vol. 11597 1159701-6 s), "Title of Paper," in Medical Imaging 2021: Computer-Aided Diagnosis, edited by Maciej A. ...
non-Parkinsonian olfactory dysfunction with structural
MRI data [11597-47]
11597 1F
Use of biplane quantitative angiographic imaging with ensemble neural networks to assess
reperfusion status during ...
segmentation of small metastatic brain tumors using liquid state machine ensemble 11597 2M Renal parenchyma segmentation in abdominal MR images based on cascaded deep convolutional neural network with ...
doi:10.1117/12.2595447
fatcat:u25cvo7adbgcxb363rsnsgnsju
Artificial Intelligence: The Future in Dentistry
2020
Indian Journal of Forensic Medicine & Toxicology
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These innovations would enable dentists to work with precision in all aspects of diagnosis and treatment planning. ...
Artificial intelligence (AI) is an area of computer technologies in influencing our lives. ...
They provide a differential diagnosis for different abnormalities in radio images in various imaging modalities. 7 Thus digital imaging modalities have enabled the usage of Computer-aided diagnosis in ...
doi:10.37506/ijfmt.v14i4.12947
fatcat:c25zqx3kpzal7hxqkgqmrcombm
A Survey on Pneumonia Detection Methods Using Computer-aided Diagnosis
2021
International Journal of Emerging Trends in Engineering Research
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Recent advances in computer-assisted identification support the diagnosis of Pneumonia using imaging. ...
Therefore, there are many activities available for diagnosing pneumonia using Computer-Aided Diagnosis. This paper provides research into the in-depth study strategies used to diagnose pneumonia. ...
RELATED WORK Much work has already be done in pneumonia detection field by using Computer-aided Diagnosis and the latest improvements in Computer-aided Diagnosis methods allow them to be used in various ...
doi:10.30534/ijeter/2021/09972021
fatcat:bqmjdndb3bevjlel4gcqkllbim
Medical image analysis with artificial neural networks
2010
Computerized Medical Imaging and Graphics
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Indexing terms: neural networks, medical imaging analysis, and intelligent computing. 2 Neural network applications in computer-aided diagnosis represent the main stream of computational intelligence in ...
After this section, four sections are organised to provide detailed descriptions of neural network applications in the areas of computer aided diagnosis, image segmentation and edge detection, image registration ...
Neural Networks for Computer Aided Detection and Diagnosis Neural networks have been incorporated into many computer-aided diagnosis systems, most of which distinguish the cancerous signs from normal tissues ...
doi:10.1016/j.compmedimag.2010.07.003
pmid:20713305
fatcat:iycrdoy4yfgjfof2ml4xk7iz6i
Implementing Precision Medicine and Artificial Intelligence in Plastic Surgery
2019
Plastic and Reconstructive Surgery, Global Open
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The algorithmic process of artificial neural networks will guide large-scale analysis of data, including features such as pattern recognition and rapid quantification, to organize and distribute data to ...
Therefore, plastic surgeons must learn how to use AI within the contexts of our practices to keep up with an evolving field in medicine. ...
will enable surgeons to formulate individualized Furthermore, AI-assisted evaluation of computed tomography (CT) angiograms, along with other smart imaging techniques, could aid surgeons in the design ...
doi:10.1097/gox.0000000000002113
pmid:31044104
pmcid:PMC6467615
fatcat:lehuglc7ordprjbqnpauvlcy4q
Deepwound: Automated Postoperative Wound Assessment and Surgical Site Surveillance through Convolutional Neural Networks
[article]
2018
arXiv
pre-print
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Convolutional neural networks (CNNs), a subgroup of artificial neural networks that have shown great promise in analyzing visual imagery, can be leveraged to categorize surgical wounds. ...
Paired with deep neural networks, they offer the capability to provide clinical insight to assist surgeons during postoperative care. ...
Thus, a rapid and portable computer aided diagnosis (CAD) tool for wound assessment will greatly assist surgeons in determining the status of a wound in a timely manner. ...
arXiv:1807.04355v1
fatcat:bftqgsdt4vcbba2o5huec3vjxe
A Study of Performance Evaluation of Convolution Neural Network for Diabetic Retinopathy
2020
International Journal of Advanced Trends in Computer Science and Engineering
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Computer-aided verification of fundus images is necessary because it permits for fast processing and bunch images can be analyzed in one shot at a time. ...
At present diagnosis for the detection of the diabetic retinopathy mainly depends on ophthalmologist who examines the retinal image and decides the patient's condition that they have diabetic retinopathy ...
The computer-adied technique will effectively aid the Clinicians for diagnosis. So by using this technologies the chances of Misdignosis will be very less whe compared to manual diagnosis. ...
doi:10.30534/ijatcse/2020/118942020
fatcat:errcchlo4bdijhzybuy4h44yhm
FASTER–RCNN for Skin Burn Analysis and Tissue Regeneration
2022
Computer systems science and engineering
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Deep neural networks can automatically assist in the extraction of features from a burn image. ...
Effective diagnosis with the help of accurate burn zone and wound depth evaluation is important for clinical efficacy. ...
By providing a cure for skin burn wounds, advancements in the fields of artificial intelligence and computer vision aid in providing a greater solution and faster recoverability. ...
doi:10.32604/csse.2022.021086
fatcat:vo2yd4ak3ffqhn4x4e4dpnrtnm
Applications of Explainable Artificial Intelligence in Diagnosis and Surgery
2022
Diagnostics
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In this review, we conducted a survey of the recent trends in medical diagnosis and surgical applications using XAI. ...
Additionally, we provide an experimental showcase on breast cancer diagnosis, and illustrate how XAI can be applied in medical XAI applications. ...
Meldo et al. proposed a lung cancer computer-aided diagnosis system with explanation sentences [37] . ...
doi:10.3390/diagnostics12020237
pmid:35204328
pmcid:PMC8870992
fatcat:fk5gbai6szf2vhf222o7p6nkqy
A Review on the Use of Artificial Intelligence in Spinal Diseases
2020
Asian Spine Journal
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Artificial neural networks (ANNs) have been used in a wide variety of real-world applications and it emerges as a promising field across various branches of medicine. ...
Our review indicates several applications of ANNs in the management of spinal diseases including (1) diagnosis and assessment of spinal disease progression in the patients with low back pain, perioperative ...
In conclusion, the computer-aided method has potential for automatic Cobb angle measurement and scoliosis diagnosis on chest X-rays. ...
doi:10.31616/asj.2020.0147
pmid:32326672
pmcid:PMC7435304
fatcat:cxdxp3jpurcgzp2hjne5mrj5qu
AN EFFICIENT SKIN CANCER PROGNOSIS STRATEGY USING DEEP LEARNING TECHNIQUES
2021
Indian Journal of Computer Science and Engineering
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In addition, the model not require much computing power to train. ...
This study provides a model of the Convolutional neural network trained for skin lesion images, from previously acquired features of the Highway Convolutional neural network (CNN). ...
For instance, neuromuscular neural networks can identify carcinoma [24] . Direct digital imaging is a popular method for medical diagnosis with new computing and device learning mechanisms. ...
doi:10.21817/indjcse/2021/v12i1/211201180
fatcat:njcau35yjjge5g6cxuioin4x4u
Segmentation and classification of burn images by color and texture information
2005
Journal of Biomedical Optics
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In this paper, a burn color image segmentation and classification system is proposed. ...
The neural network classifies burns into three types of burn depths: superficial dermal, deep dermal, and full thickness. ...
Torre, and the burn unit of Virgen del Rocío Hospital, Seville ͑Spain͒ for providing us with the burn wound photographs and their medical advice, and the CICYT Spain ͑Project No. ...
doi:10.1117/1.1921227
pmid:16229658
fatcat:monpwvaj7nexxbwoscdzvbhyza
Burn image segmentation based on Mask Regions with Convolutional Neural Network deep learning framework: more accurate and more convenient
2019
Burns & Trauma
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Moreover, this framework just needs a suitable burn wound image when analyzing the burn wound. It is more convenient and more suitable when using in clinics compared with the traditional methods. ...
We designed this deep learning segmentation framework based on the Mask Regions with Convolutional Neural Network (Mask R-CNN). ...
Availability of data and materials The data used in this study cannot be shared in compliance with Wuhan 607 Hospital NO.3 ethics and confidentiality. ...
doi:10.1186/s41038-018-0137-9
pmid:30859107
pmcid:PMC6394103
fatcat:umadn2wfjncsnpwxgraqbkai3i
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