Retinal photograph-based deep learning algorithms for myopia and a blockchain platform to facilitate artificial intelligence medical research: a retrospective multicohort study

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

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Reviewing the relationship between machines and radiology: the application of artificial intelligence

Acta Radiologica Open ◽

10.1177/2058460121990296 ◽

2021 ◽

Vol 10 (2) ◽

pp. 205846012199029

Author(s):

Rani Ahmad

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Deep Learning ◽

Health Care Professionals ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Health Science ◽

Computer Algorithms ◽

Learning Models ◽

Specificity And Sensitivity

Background The scope and productivity of artificial intelligence applications in health science and medicine, particularly in medical imaging, are rapidly progressing, with relatively recent developments in big data and deep learning and increasingly powerful computer algorithms. Accordingly, there are a number of opportunities and challenges for the radiological community. Purpose To provide review on the challenges and barriers experienced in diagnostic radiology on the basis of the key clinical applications of machine learning techniques. Material and Methods Studies published in 2010–2019 were selected that report on the efficacy of machine learning models. A single contingency table was selected for each study to report the highest accuracy of radiology professionals and machine learning algorithms, and a meta-analysis of studies was conducted based on contingency tables. Results The specificity for all the deep learning models ranged from 39% to 100%, whereas sensitivity ranged from 85% to 100%. The pooled sensitivity and specificity were 89% and 85% for the deep learning algorithms for detecting abnormalities compared to 75% and 91% for radiology experts, respectively. The pooled specificity and sensitivity for comparison between radiology professionals and deep learning algorithms were 91% and 81% for deep learning models and 85% and 73% for radiology professionals (p < 0.000), respectively. The pooled sensitivity detection was 82% for health-care professionals and 83% for deep learning algorithms (p < 0.005). Conclusion Radiomic information extracted through machine learning programs form images that may not be discernible through visual examination, thus may improve the prognostic and diagnostic value of data sets.

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Advanced Deep Learning Model

10.5121/csit.2021.111707 ◽

2021 ◽

Author(s):

Yew Kee Wong

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Decision Making ◽

Deep Learning ◽

Learning Algorithms ◽

Learning Model ◽

Intelligence Analysis ◽

Basic Parameters ◽

Deep Learning Model ◽

Learning Sets

Deep learning is a type of machine learning that trains a computer to perform human-like tasks, such as recognizing speech, identifying images or making predictions. Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using many layers of processing. This paper aims to illustrate some of the different deep learning algorithms and methods which can be applied to artificial intelligence analysis, as well as the opportunities provided by the application in various decision making domains.

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Comprehensive Overview of Neural Networks and Its Applications in Autonomous Vehicles

Computational Intelligence in the Internet of Things - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-7955-7.ch007 ◽

2019 ◽

pp. 159-173

Author(s):

Jay Rodge ◽

Swati Jaiswal

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Neural Networks ◽

Deep Learning ◽

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Learning Algorithms ◽

Activation Functions ◽

Comprehensive Overview ◽

Unit Component

Deep learning and Artificial intelligence (AI) have been trending these days due to the capability and state-of-the-art results that they provide. They have replaced some highly skilled professionals with neural network-powered AI, also known as deep learning algorithms. Deep learning majorly works on neural networks. This chapter discusses about the working of a neuron, which is a unit component of neural network. There are numerous techniques that can be incorporated while designing a neural network, such as activation functions, training, etc. to improve its features, which will be explained in detail. It has some challenges such as overfitting, which are difficult to neglect but can be overcome using proper techniques and steps that have been discussed. The chapter will help the academician, researchers, and practitioners to further investigate the associated area of deep learning and its applications in the autonomous vehicle industry.

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Promising Artificial Intelligence–Machine Learning–Deep Learning Algorithms in Ophthalmology

Asia-Pacific Journal of Ophthalmology ◽

10.1097/01.apo.0000586388.81551.d0 ◽

2019 ◽

Vol 8 (5) ◽

pp. 417

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Deep Learning ◽

Learning Algorithms

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Artificial Intelligence in Corporate Sustainability: Using LSTM and GRU for Going Concern Prediction

Sustainability ◽

10.3390/su132111631 ◽

2021 ◽

Vol 13 (21) ◽

pp. 11631

Author(s):

Der-Jang Chi ◽

Chien-Chou Chu

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Prediction Models ◽

Short Term Memory ◽

Corporate Sustainability ◽

Learning Algorithms ◽

Error Rates ◽

Type I ◽

Going Concern ◽

Financial Variables

“Going concern” is a professional term in the domain of accounting and auditing. The issuance of appropriate audit opinions by certified public accountants (CPAs) and auditors is critical to companies as a going concern, as misjudgment and/or failure to identify the probability of bankruptcy can cause heavy losses to stakeholders and affect corporate sustainability. In the era of artificial intelligence (AI), deep learning algorithms are widely used by practitioners, and academic research is also gradually embarking on projects in various domains. However, the use of deep learning algorithms in the prediction of going concern remains limited. In contrast to those in the literature, this study uses long short-term memory (LSTM) and gated recurrent unit (GRU) for learning and training, in order to construct effective and highly accurate going-concern prediction models. The sample pool consists of the Taiwan Stock Exchange Corporation (TWSE) and the Taipei Exchange (TPEx) listed companies in 2004–2019, including 86 companies with going concern doubt and 172 companies without going concern doubt. In other words, 258 companies in total are sampled. There are 20 research variables, comprising 16 financial variables and 4 non-financial variables. The results are based on performance indicators such as accuracy, precision, recall/sensitivity, specificity, F1-scores, and Type I and Type II error rates, and both the LSTM and GRU models perform well. As far as accuracy is concerned, the LSTM model reports 96.15% accuracy while GRU shows 94.23% accuracy.

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A Review on Cyber Security Mechanisms Using Machine and Deep Learning Algorithms

Handbook of Research on Machine and Deep Learning Applications for Cyber Security - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-5225-9611-0.ch002 ◽

2020 ◽

pp. 23-41 ◽

Cited By ~ 1

Author(s):

Thiyagarajan P.

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Deep Learning ◽

Information Security ◽

Cyber Security ◽

Learning Algorithms ◽

The Other ◽

The Internet ◽

Learning Techniques

Digitalization is the buzz word today by which every walk of our life has been computerized, and it has made our life more sophisticated. On one side, we are enjoying the privilege of digitalization. On the other side, security of our information in the internet is the most concerning element. A variety of security mechanisms, namely cryptography, algorithms which provide access to protected information, and authentication including biometric and steganography, provide security to our information in the Internet. In spite of the above mechanisms, recently artificial intelligence (AI) also contributes towards strengthening information security by providing machine learning and deep learning-based security mechanisms. The artificial intelligence (AI) contribution to cyber security is important as it serves as a provoked reaction and a response to hackers' malicious actions. The purpose of this chapter is to survey recent papers which are contributing to information security by using machine learning and deep learning techniques.

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A Review of Deep Machine Learning

International Journal of Engineering Research in Africa ◽

10.4028/www.scientific.net/jera.24.124 ◽

2016 ◽

Vol 24 ◽

pp. 124-136 ◽

Cited By ~ 15

Author(s):

Ben Bright Benuwa ◽

Yong Zhao Zhan ◽

Benjamin Ghansah ◽

Dickson Keddy Wornyo ◽

Frank Banaseka Kataka

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Pattern Recognition ◽

Deep Learning ◽

Paradigm Shift ◽

State Of The Art ◽

Learning Algorithms ◽

Algorithm Design ◽

Prior State ◽

Art Techniques

The rapid increase of information and accessibility in recent years has activated a paradigm shift in algorithm design for artificial intelligence. Recently, deep learning (a surrogate of Machine Learning) have won several contests in pattern recognition and machine learning. This review comprehensively summarises relevant studies, much of it from prior state-of-the-art techniques. This paper also discusses the motivations and principles regarding learning algorithms for deep architectures.

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Autism Spectrum Disorder Classification Using Deep Learning

International Journal of Online and Biomedical Engineering (iJOE) ◽

10.3991/ijoe.v17i08.24603 ◽

2021 ◽

Vol 17 (08) ◽

pp. 103

Author(s):

Abdulrazak Yahya Saleh ◽

Lim Huey Chern

Keyword(s):

Artificial Intelligence ◽

Autism Spectrum Disorder ◽

Deep Learning ◽

Learning Algorithm ◽

Learning Algorithms ◽

Image Data ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Support Vector ◽

Wide Range

<p class="0abstract">The goal of this paper is to evaluate the deep learning algorithm for people placed in the Autism Spectrum Disorder (ASD) classification. ASD is a developmental disability that causes the affected people to have significant communication, social, and behavioural challenges. People with autism are saddled with communication problems, difficulties in social interaction and displaying repetitive behaviours. Several methods have been used to classify the ASD from non-ASD people. However, there is a need to explore more algorithms that can yield better classification performance. Recently, deep learning methods have significantly sharpened the cutting edge of learning algorithms in a wide range of artificial intelligence tasks. These artificial intelligence tasks refer to object detection, speech recognition, and machine translation. In this research, the convolutional neural network (CNN) is employed. This algorithm is used to find processes that can classify ASD with a higher level of accuracy. The image data is pre-processed; the CNN algorithm is then applied to classify the ASD and non-ASD, and the steps of implementing the CNN algorithm are clearly stated. Finally, the effectiveness of the algorithm is evaluated based on the accuracy performance. The support vector machine (SVM) is utilised for the purpose of comparison. The CNN algorithm produces better results with an accuracy of 97.07%, compared with the SVM algorithm. In the future, different types of deep learning algorithms need to be applied, and different datasets can be tested with different hyper-parameters to produce more accurate ASD classifications.</p>

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A Study to Find Facts Behind Preprocessing on Deep Learning Algorithms

Journal of Innovative Image Processing - December 2019 ◽

10.36548/jiip.2021.1.006 ◽

2021 ◽

Vol 3 (1) ◽

pp. 66-74

Author(s):

Ranganathan G

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Computer System ◽

Learning Algorithms ◽

Human Beings ◽

Step Over ◽

Near Future ◽

Speed And Accuracy

In the near future, deep learning algorithms will be incorporated in several applications for assisting the human beings. The deep learning algorithms have the tendency to allow a computer to work on its assumption. Most of the deep learning algorithms mimic the human brain’s neuron connection to leverage an artificial intelligence to the computer system. This helps to improve the operational speed and accuracy on several critical tasks. This paper projects the blocks, which are required for the incorporation of deep learning based algorithm. Also, the paper attempts to deeply analyze the necessity of the preprocessing step over several deep learning based applications.

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