APPLICATION OF ARTIFICIAL INTELLIGENCE-BASED IMAGE ANALYSIS IN BIOINFORMATICS

2021 ◽  
Author(s):  
Zlatan Car ◽  
◽  
Nikola Anđelić ◽  
Ivan Lorencin ◽  
Jelena Musulin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Clinical Practice ◽  
Image Data ◽  
Algorithm Analysis ◽  
Diagnostic Systems ◽  
Common Procedure ◽  
Magnetic Resonance Imaging Mri ◽  
Standard Practices

The collection of image data is an extremely common procedure in clinical practice today. Many of the diagnostic approaches generate such data – computed tomography (CT), X-ray radiography, magnetic resonance imaging (MRI), and others. This data collection process allows for the use of computer vision approaches to be applied with the goal of analysis and diagnostics. Artificial Intelligence (AI) based algorithms have repeatedly been shown to be the best performing computer vision algorithms, in many fields including medicine. AI-based – or more precisely machine learning (ML) based, algorithms have capabilities which allow them to learn the patterns contained in the data from the data itself. Among the best performing algorithms are artificial neural networks (ANNs), or more precisely convolutional neural networks (CNNs). Their pitfall is the need for the large amounts of data – but as it has been previously mentioned, the amount of data collected in today’s clinical practice is large and ever increasing. This allows for the development of Smart Diagnostic systems which are meant to serve as support systems to the health professionals. In this paper first, the standard practices and review of the field is given – with the focus on challenges and best practices. Then, multiple examples of the research applying AI-based algorithm analysis are given – including diagnostics of various cancer types (bladder and oral) as well as COVID-19 severity diagnostics and image quality determination.

scholarly journals Deep learning-enabled medical computer vision

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Andre Esteva ◽  
Katherine Chou ◽  
Serena Yeung ◽  
Nikhil Naik ◽  
Ali Madani ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Medical Imaging ◽  
Real World ◽  
Recent Progress ◽  
Medical Applications ◽  
Modern Computer ◽  
Medical Computer

AbstractA decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields—including medicine—to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques—powered by deep learning—for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare. Next, we discuss several example medical imaging applications that stand to benefit—including cardiology, pathology, dermatology, ophthalmology–and propose new avenues for continued work. We then expand into general medical video, highlighting ways in which clinical workflows can integrate computer vision to enhance care. Finally, we discuss the challenges and hurdles required for real-world clinical deployment of these technologies.

scholarly journals Artificial Intelligence in Public Health Dentistry

2021 ◽  
Vol 5 (9) ◽  
pp. RV1-RV5
Author(s):  
Sahrish Tariq ◽  
Nidhi Gupta ◽  
Preety Gupta ◽  
Aditi Sharma
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Clinical Practice ◽  
Correct Diagnosis ◽  
Scientific Research ◽  
Modern Science ◽  
Appropriate Technology ◽  
Human Element ◽  
New Knowledge

The educational needs must drive the development of the appropriate technology”. They should not be viewed as toys for enthusiasts. Nevertheless, the human element must never be dismissed. Scientific research will continue to offer exciting technologies and effective treatments. For the profession and the patients, it serves to benefit fully from modern science, new knowledge and technologies must be incorporated into the mainstream of dental education. The technologies of modern science have astonished and intrigued our imagination. Correct diagnosis is the key to a successful clinical practice. In this regard, adequately trained neural networks can be a boon to diagnosticians, especially in conditions having multifactorial etiology.

scholarly journals Big data, machine learning and artificial intelligence: a neurologist’s guide

2020 ◽  
pp. practneurol-2020-002688
Author(s):  
Stephen D Auger ◽  
Benjamin M Jacobs ◽  
Ruth Dobson ◽  
Charles R Marshall ◽  
Alastair J Noyce
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Big Data ◽  
Clinical Practice ◽  
Clinical Data ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Basic Principles ◽  
Biological Neural Networks

Modern clinical practice requires the integration and interpretation of ever-expanding volumes of clinical data. There is, therefore, an imperative to develop efficient ways to process and understand these large amounts of data. Neurologists work to understand the function of biological neural networks, but artificial neural networks and other forms of machine learning algorithm are likely to be increasingly encountered in clinical practice. As their use increases, clinicians will need to understand the basic principles and common types of algorithm. We aim to provide a coherent introduction to this jargon-heavy subject and equip neurologists with the tools to understand, critically appraise and apply insights from this burgeoning field.

scholarly journals Technical note: AI-Track-tive: automated fission track recognition using computer vision (Artificial Intelligence)

2020 ◽  
Author(s):  
Simon Nachtergaele ◽  
Johan De Grave
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Neural Networks ◽  
Fission Track ◽  
Detection Algorithm ◽  
Technical Note ◽  
Success Rates ◽  
Automatic Counting ◽  
First Time

Abstract. Artificial intelligence techniques such as deep neural networks and computer vision are developed for fission track recognition and included in a computer program for the first time. These deep neural networks use the Yolov3 object detection algorithm, which is currently one of the most powerful and fastest object recognition algorithms. These deep neural networks can be used in new software called AI-Track-tive. The developed program successfully finds most of the fission tracks in the microscope images, however, the user still needs to supervise the automatic counting. The success rates of the automatic recognition range from 70 % to 100 % depending on the areal track densities in apatite and (muscovite) external detector. The success rate generally decreases for images with high areal track densities, because overlapping tracks are less easily recognizable for computer vision techniques.

scholarly journals Frontiers of Computer Vision Technologies on Real Estate Property Photographs and Floorplans

2021 ◽  
pp. 325-337
Author(s):  
Yoji Kiyota
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Deep Learning ◽  
Real Estate ◽  
Image Data ◽  
Learning Technologies ◽  
Data Sets ◽  
Academic Communities ◽  
Estate Property ◽  
Extract Information

AbstractThis article describes frontier efforts to apply deep learning technologies, which is the greatest innovation of research on artificial intelligence and computer vision, to image data such as real estate property photographs and floorplans. Specifically, attempts to detect property photographs that violate regulations or were misclassified, or to extract information that can be used as new recommendation features from property photographs, were mentioned. Besides, this article introduces an innovation created by providing data sets for academic communities.

scholarly journals Visual Modeling of Data using Convolutional Neural Networks

2019 ◽  
Vol 9 (1) ◽  
pp. 4938-4942
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Recurrent Neural Networks ◽  
Feedforward Neural Networks ◽  
Correct Choice ◽  
Activation Functions ◽  
Visual Modeling ◽  
Training Images ◽  
Canadian Institute

Artificial Intelligence has been showing monumental growth in filling the gap between the capabilities of humans and machines. Researchers and scientists work on many aspects to make new things happen. Computer Vision is one of them. To make the system to visualize, neural networks are used. Some of the well-known Neural Networks include CNN, Feedforward Neural Networks (FNN), and Recurrent Neural Networks (RNN) and so on. Among them, CNN is the correct choice for computer vision because they learn relevant features from an image or video similar to the human brain. In this paper, the dataset used is CIFAR-10 (Canadian Institute for Advanced Research) which contains 60,000 images in the size of 32x32. Those images are divided into 10 different classes which contains both training and testing images. The training images are 50,000 and testing images are 10,000. The ten different classes contain airplanes, automobiles, birds, cat, ship, truck, deer, dog, frog and horse images. This paper was mainly concentrated on improving performance using normalization layers and comparing the accuracy achieved using different activation functions like ReLU and Tanh.

scholarly journals Artificial Intelligence Explained for Nonexperts

2020 ◽  
Vol 24 (01) ◽  
pp. 003-011 ◽  
Author(s):  
Narges Razavian ◽  
Florian Knoll ◽  
Krzysztof J. Geras
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Clinical Practice ◽  
Medical Imaging ◽  
Deep Neural Networks ◽  
Large Data ◽  
Large Data Sets ◽  
Natural Images ◽  
Data Sets ◽  
Current State

AbstractArtificial intelligence (AI) has made stunning progress in the last decade, made possible largely due to the advances in training deep neural networks with large data sets. Many of these solutions, initially developed for natural images, speech, or text, are now becoming successful in medical imaging. In this article we briefly summarize in an accessible way the current state of the field of AI. Furthermore, we highlight the most promising approaches and describe the current challenges that will need to be solved to enable broad deployment of AI in clinical practice.

P1846HISTORY OF ARTIFICIAL INTELLIGENCE AND ITS APPLICATION IN NEPHROLOGY

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Danijela Tasic ◽  
Milos Milovancevic ◽  
Katarina Djordjevic ◽  
Slobodanka Galovic ◽  
Zorica Dimitrijevic ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Clinical Practice ◽  
Technological Development ◽  
Basic Research ◽  
Good Clinical Practice ◽  
Von Neumann ◽  
Hermann Von Helmholtz ◽  
Norbert Wiener ◽  
New Good

Abstract Background and Aims The application of artificial intelligence and neural networks in medicine is used to help solve problems that cannot be handled by the classical approach. The common name “cybernetics” encompassed the fields of management, information technology and biomedicine, but these disciplines continued to evolve independently due to the explosion of new knowledge. Over time, the development of neural networks has been turbulent and is now widely used in various fields of medicine and even in nephrology. The aim of the paper is to analyze the history of the development of artificial intelligence and its application in nephrology. Method Data were collected from books, magazines, encyclopedias and databases. Results Basic research on cybernetics and medicine was done by Golgi and Kelley doctors after Isaak Newton and Hermann von Helmholtz. The first theoretical mathematical models were derived in 1943 by Warren Mc Culloch and Walter Pitts. A few years later, a more contemporary contribution to the development of neural networks was given by Norbert Wiener and John von Neumann because they thought that research into biomedicine based on human brain function would be very interesting. In addition, in 1948 Norbert Wiener was the first to publish a work explaining the term cybernetics. At that time, the first experiments were made and new theories in the field of artificial intelligence were put forward by Marvin Misnki. The first training of neurons and the basis of all methods for training neurons was described by the Canadian Donald O Hebb. After the first successful neurocomputer in 1957, on which Rosenblatt worked, scientists have perfected various models of neural networks to this day. So far, mostly retrospective studies have been done in clinical nephrology, transplantation and dialysis with the help of algorithms used in neural networks. Particularly complex nephrologic patient relationships as well as assistance with timely implementation of new good clinical practice guidelines, patient prediction in at least the next month, and patient selection for palliative care are just some segments in nephrology that require the introduction of such tools into daily clinical practice with the aim of sensitive patient populations have better treatment outcomes, with physicians having more comprehensive insight and control over the mass of data. Conclusion Today‘s application of artificial intelligence in nephrology is based on retrospective research. The dizzying rise in technological development so far will allow the use of cybernetics and available tools based on neural network algorithms to enable and improve the nephrologists’ dedication and effectiveness.

scholarly journals An efficient classification of flower images with convolutional neural networks

2017 ◽  
Vol 7 (1.1) ◽  
pp. 384 ◽  
Author(s):  
M V.D. Prasad ◽  
B JwalaLakshmamma ◽  
A Hari Chandana ◽  
K Komali ◽  
M V.N. Manoja ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Recognition Rate ◽  
Image Data ◽  
Image Database

Machine learning is penetrating most of the classification and recognition tasks performed by a computer. This paper proposes the classification of flower images using a powerful artificial intelligence tool, convolutional neural networks (CNN). A flower image database with 9500 images is considered for the experimentation. The entire database is sub categorized into 4. The CNN training is initiated in five batches and the testing is carried out on all the for datasets. Different CNN architectures were designed and tested with our flower image data to obtain better accuracy in recognition. Various pooling schemes were implemented to improve the classification rates. We achieved 97.78% recognition rate compared to other classifier models reported on the same dataset.

scholarly journals Automatic Image Captioning Using Neural Networks

2020 ◽  
Vol 3 (1) ◽  
pp. 138-146
Author(s):  
Subash Pandey ◽  
Rabin Kumar Dhamala ◽  
Bikram Karki ◽  
Saroj Dahal ◽  
Rama Bastola
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Natural Language ◽  
Language Processing ◽  
Model Performance ◽  
Image Description ◽  
Image Captioning ◽  
Top Down

 Automatically generating a natural language description of an image is a major challenging task in the field of artificial intelligence. Generating description of an image bring together the fields: Natural Language Processing and Computer Vision. There are two types of approaches i.e. top-down and bottom-up. For this paper, we approached top-down that starts from the image and converts it into the word. Image is passed to Convolutional Neural Network (CNN) encoder and the output from it is fed further to Recurrent Neural Network (RNN) decoder that generates meaningful captions. We generated the image description by passing the real time images from the camera of a smartphone as well as tested with the test images from the dataset. To evaluate the model performance, we used BLEU (Bilingual Evaluation Understudy) score and match predicted words to the original caption.

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