CONVOLUTIONAL NEURAL NETWORKS, ANALYTICAL ALGORITHMS, AND PERSONALIZED HEALTH CARE: EMBRACING THE MASSIVE DATA ANALYSIS CAPABILITIES OF DEEP LEARNING ARTIFICIAL INTELLIGENCE SYSTEMS TO COMPLEMENT AND IMPROVE MEDICAL SERVICES

2018 ◽  
Vol 5 (2) ◽  
pp. 52 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Health Care ◽  
Deep Learning ◽  
Data Analysis ◽  
Convolutional Neural Networks ◽  
Massive Data ◽  
Personalized Health ◽  
Personalized Health Care ◽  
Artificial Intelligence Systems

scholarly journals A primer on deep learning and convolutional neural networks for clinicians

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Lara Lloret Iglesias ◽  
Pablo Sanz Bellón ◽  
Amaia Pérez del Barrio ◽  
Pablo Menéndez Fernández-Miranda ◽  
David Rodríguez González ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Learning Capabilities ◽  
Computer Vision Applications ◽  
Fully Connected ◽  
Imaging Perspective ◽  
Adaptive Momentum

AbstractDeep learning is nowadays at the forefront of artificial intelligence. More precisely, the use of convolutional neural networks has drastically improved the learning capabilities of computer vision applications, being able to directly consider raw data without any prior feature extraction. Advanced methods in the machine learning field, such as adaptive momentum algorithms or dropout regularization, have dramatically improved the convolutional neural networks predicting ability, outperforming that of conventional fully connected neural networks. This work summarizes, in an intended didactic way, the main aspects of these cutting-edge techniques from a medical imaging perspective.

Train Deep Learning Models using subsurface geological images datasets

2021 ◽  
Author(s):  
Ramy Abdallah ◽  
Clare E. Bond ◽  
Robert W.H. Butler
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Learning Models ◽  
Structural Interpretation ◽  
Performance Accuracy ◽  
Learning Techniques ◽  
Machine Learning Models

<p>Machine learning is being presented as a new solution for a wide range of geoscience problems. Primarily machine learning has been used for 3D seismic data processing, seismic facies analysis and well log data correlation. The rapid development in technology with open-source artificial intelligence libraries and the accessibility of affordable computer graphics processing units (GPU) makes the application of machine learning in geosciences increasingly tractable. However, the application of artificial intelligence in structural interpretation workflows of subsurface datasets is still ambiguous. This study aims to use machine learning techniques to classify images of folds and fold-thrust structures. Here we show that convolutional neural networks (CNNs) as supervised deep learning techniques provide excellent algorithms to discriminate between geological image datasets. Four different datasets of images have been used to train and test the machine learning models. These four datasets are a seismic character dataset with five classes (faults, folds, salt, flat layers and basement), folds types with three classes (buckle, chevron and conjugate), fault types with three classes (normal, reverse and thrust) and fold-thrust geometries with three classes (fault bend fold, fault propagation fold and detachment fold). These image datasets are used to investigate three machine learning models. One Feedforward linear neural network model and two convolutional neural networks models (Convolution 2d layer transforms sequential model and Residual block model (ResNet with 9, 34, and 50 layers)). Validation and testing datasets forms a critical part of testing the model’s performance accuracy. The ResNet model records the highest performance accuracy score, of the machine learning models tested. Our CNN image classification model analysis provides a framework for applying machine learning to increase structural interpretation efficiency, and shows that CNN classification models can be applied effectively to geoscience problems. The study provides a starting point to apply unsupervised machine learning approaches to sub-surface structural interpretation workflows.</p>

scholarly journals Deep learning in Emergency Medicine: Recent contributions and methodological challenges

2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Francesco Faita
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Emergency Medicine ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Recurrent Neural Networks ◽  
Computational Models ◽  
Clinical Usefulness ◽  
Human Knowledge ◽  
Deep Convolutional Neural Networks

In the last few years, artificial intelligence (AI) technology has grown dramatically impacting several fields of human knowledge and medicine in particular. Among other approaches, deep learning, which is a subset of AI based on specific computational models, such as deep convolutional neural networks and recurrent neural networks, has shown exceptional performance in images and signals processing. Accordingly, emergency medicine will benefit from the adoption of this technology. However, a particular attention should be devoted to the review of these papers in order to exclude overoptimistic results from clinically transferable ones. We presented a group of studies recently published on PubMed and selected by keywords ‘deep learning emergency medicine’ and ‘artificial intelligence emergency medicine’ with the aim of highlighting their methodological strengths and weaknesses, as well as their clinical usefulness.

scholarly journals SOIL MOISTURE RETRIEVAL USING CONVOLUTIONAL NEURAL NETWORKS: APPLICATION TO PASSIVE MICROWAVE REMOTE SENSING

2018 ◽  
Vol XLII-3 ◽  
pp. 583-586 ◽  
Author(s):  
Z. Hu ◽  
L. Xu ◽  
B. Yu
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Soil Moisture ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Massive Data ◽  
Passive Microwave Remote Sensing ◽  
Global Soil

A empirical model is established to analyse the daily retrieval of soil moisture from passive microwave remote sensing using convolutional neural networks (CNN). Soil moisture plays an important role in the water cycle. However, with the rapidly increasing of the acquiring technology for remotely sensed data, it's a hard task for remote sensing practitioners to find a fast and convenient model to deal with the massive data. In this paper, the AMSR-E brightness temperatures are used to train CNN for the prediction of the European centre for medium-range weather forecasts (ECMWF) model. Compared with the classical inversion methods, the deep learning-based method is more suitable for global soil moisture retrieval. It is very well supported by graphics processing unit (GPU) acceleration, which can meet the demand of massive data inversion. Once the model trained, a global soil moisture map can be predicted in less than 10 seconds. What's more, the method of soil moisture retrieval based on deep learning can learn the complex texture features from the big remote sensing data. In this experiment, the results demonstrates that the CNN deployed to retrieve global soil moisture can achieve a better performance than the support vector regression (SVR) for soil moisture retrieval.

iOS App and Architecture of Convolutional Neural Networks

2020 ◽  
pp. 1-22
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Neural Organization ◽  
Computing Environments ◽  
Audio Video ◽  
Developing Pattern

Deep convolutional neural networks (CNN) have attracted many attentions of researchers in the field of artificial intelligence. Based on several well-known architectures, more researchers and designers have joined the field of applying deep learning and devising a large number of CNNs for processing datasets of interesting. Equipped with modern audio, video, screen-touching components, and other sensors for online pattern recognition, the iOS mobile devices provide developers and users friendly testing and powerful computing environments. This chapter introduces the trend of developing pattern recognition CNN Apps on iOS devices and the neural organization of convolutional neural networks. Deep learning in Matlab and executing CNN models on iOS devices are introduced following the motivation of combining mathematical modelling and computation with neural architectures for developing pattern recognition iOS apps. This chapter also gives contexts of discussing typical hidden layers in the CNN architecture.

scholarly journals A Spectrogram Based Novel Approach for Arrhythmia Detection with Convolutional Neural Networks

2021 ◽  
Author(s):  
Sneha Rao ◽  
Vishwa Mohan Singh ◽  
Siddhivinayak Kulkarni ◽  
Vibhor Saran
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Medical Diagnosis ◽  
Light Weight ◽  
Arrhythmia Detection ◽  
Ecg Signals ◽  
Novel Approach ◽  
Convolution Model

Abstract ECG is one of the most important medical scans which is used for diagnosis of various heart related conditions and diseases. One of the most common of these is arrhythmia, which is caused by the irregularity of the heart beats. Artificial Intelligence has had a major impact in the field of vital monitoring and autonomous medical diagnosis. Therefore, a lot of work has demonstrated its effectiveness in arrhythmia detection. In this paper, we propose a method that tries to improve upon the accuracy of such models with the help of a light weight deep learning architecture that utilized 2D Separable CNN with a group of graphical representations of the ECG signals like the STFT, CWT and MFCC. Our model has achieved an accuracy of 97.41 and an F1 score of 88.20 on a processed version of the MIT-BIH dataset and takes on an average 7.93 times less calculations compared to a simple 2D Convolution model.

scholarly journals Artificial Intelligence in Health Care: Bibliometric Analysis (Preprint)

2020 ◽  
Author(s):  
Yuqi Guo ◽  
Zhichao Hao ◽  
Shichong Zhao ◽  
Jiaqi Gong ◽  
Fan Yang
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Health Care ◽  
Growth Rate ◽  
Bibliometric Analysis ◽  
Convolutional Neural Networks ◽  
Health Care Research ◽  
Support Vector ◽  
Research Papers ◽  
Care Research

BACKGROUND As a critical driving power to promote health care, the health care–related artificial intelligence (AI) literature is growing rapidly. OBJECTIVE The purpose of this analysis is to provide a dynamic and longitudinal bibliometric analysis of health care–related AI publications. METHODS The Web of Science (Clarivate PLC) was searched to retrieve all existing and highly cited AI-related health care research papers published in English up to December 2019. Based on bibliometric indicators, a search strategy was developed to screen the title for eligibility, using the abstract and full text where needed. The growth rate of publications, characteristics of research activities, publication patterns, and research hotspot tendencies were computed using the HistCite software. RESULTS The search identified 5235 hits, of which 1473 publications were included in the analyses. Publication output increased an average of 17.02% per year since 1995, but the growth rate of research papers significantly increased to 45.15% from 2014 to 2019. The major health problems studied in AI research are cancer, depression, Alzheimer disease, heart failure, and diabetes. Artificial neural networks, support vector machines, and convolutional neural networks have the highest impact on health care. Nucleosides, convolutional neural networks, and tumor markers have remained research hotspots through 2019. CONCLUSIONS This analysis provides a comprehensive overview of the AI-related research conducted in the field of health care, which helps researchers, policy makers, and practitioners better understand the development of health care–related AI research and possible practice implications. Future AI research should be dedicated to filling in the gaps between AI health care research and clinical applications.

scholarly journals COVID-19 Detection using Deep Learning

2020 ◽  
Vol 6 (12) ◽  
pp. 421-425
Author(s):  
Vandit Gupta
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
X Ray ◽  
Processing Data ◽  
Neural Learning ◽  
Radio Imaging ◽  
Augmentation Techniques ◽  
Self Driving Cars

Deep learning is an artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning and recognizing patterns from data that is unstructured or unlabelled. It is also known as deep neural learning or deep neural network. Convolutional Neural Networks (ConvNets or CNNs) are a category of Neural Networks that have proven very effective in areas such as image recognition and classification. ConvNets have been successful in identifying faces, objects and traffic signs apart from powering vision in robots and self-driving cars. ConvNets can also be used for Radio Imaging which helps in disease detection. This paper helps in detecting COVID-19 from the X-ray images provided to the model using Convolutional Neural Networks (CNN) and image augmentation techniques.

scholarly journals Artificial Intelligence-Based Polyp Detection in Colonoscopy: Where Have We Been, Where Do We Stand, and Where Are We Headed?

2020 ◽  
Vol 36 (6) ◽  
pp. 428-438
Author(s):  
Thomas Wittenberg ◽  
Martin Raithel
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Big Data ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Lesion Detection ◽  
Screening Colonoscopy ◽  
Computer Assisted ◽  
Polyp Detection ◽  
The Past

<b><i>Background:</i></b> In the past, image-based computer-assisted diagnosis and detection systems have been driven mainly from the field of radiology, and more specifically mammography. Nevertheless, with the availability of large image data collections (known as the “Big Data” phenomenon) in correlation with developments from the domain of artificial intelligence (AI) and particularly so-called deep convolutional neural networks, computer-assisted detection of adenomas and polyps in real-time during screening colonoscopy has become feasible. <b><i>Summary:</i></b> With respect to these developments, the scope of this contribution is to provide a brief overview about the evolution of AI-based detection of adenomas and polyps during colonoscopy of the past 35 years, starting with the age of “handcrafted geometrical features” together with simple classification schemes, over the development and use of “texture-based features” and machine learning approaches, and ending with current developments in the field of deep learning using convolutional neural networks. In parallel, the need and necessity of large-scale clinical data will be discussed in order to develop such methods, up to commercially available AI products for automated detection of polyps (adenoma and benign neoplastic lesions). Finally, a short view into the future is made regarding further possibilities of AI methods within colonoscopy. <b><i>Key Messages:</i></b> Research<b><i></i></b>of<b><i></i></b>image-based lesion detection in colonoscopy data has a 35-year-old history. Milestones such as the Paris nomenclature, texture features, big data, and deep learning were essential for the development and availability of commercial AI-based systems for polyp detection.

Image Classification Based On CNN: A Survey

2021 ◽  
pp. PP. 18-50
Author(s):  
Ahmed A. Elngar ◽  
◽  
◽  
◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Vision System ◽  
Machine Learning Algorithms ◽  
Traffic Lights

Computer vision is one of the fields of computer science that is one of the most powerful and persuasive types of artificial intelligence. It is similar to the human vision system, as it enables computers to recognize and process objects in pictures and videos in the same way as humans do. Computer vision technology has rapidly evolved in many fields and contributed to solving many problems, as computer vision contributed to self-driving cars, and cars were able to understand their surroundings. The cameras record video from different angles around the car, then a computer vision system gets images from the video, and then processes the images in real-time to find roadside ends, detect other cars, and read traffic lights, pedestrians, and objects. Computer vision also contributed to facial recognition; this technology enables computers to match images of people’s faces to their identities. which these algorithms detect facial features in images and then compare them with databases. Computer vision also play important role in Healthcare, in which algorithms can help automate tasks such as detecting Breast cancer, finding symptoms in x-ray, cancerous moles in skin images, and MRI scans. Computer vision also contributed to many fields such as image classification, object discovery, motion recognition, subject tracking, and medicine. The rapid development of artificial intelligence is making machine learning more important in his field of research. Use algorithms to find out every bit of data and predict the outcome. This has become an important key to unlocking the door to AI. If we had looked to deep learning concept, we find deep learning is a subset of machine learning, algorithms inspired by structure and function of the human brain called artificial neural networks, learn from large amounts of data. Deep learning algorithm perform a task repeatedly, each time tweak it a little to improve the outcome. So, the development of computer vision was due to deep learning. Now we'll take a tour around the convolution neural networks, let us say that convolutional neural networks are one of the most powerful supervised deep learning models (abbreviated as CNN or ConvNet). This name ;convolutional ; is a token from a mathematical linear operation between matrixes called convolution. CNN structure can be used in a variety of real-world problems including, computer vision, image recognition, natural language processing (NLP), anomaly detection, video analysis, drug discovery, recommender systems, health risk assessment, and time-series forecasting. If we look at convolutional neural networks, we see that CNN are similar to normal neural networks, the only difference between CNN and ANN is that CNNs are used in the field of pattern recognition within images mainly. This allows us to encode the features of an image into the structure, making the network more suitable for image-focused tasks, with reducing the parameters required to set-up the model. One of the advantages of CNN that it has an excellent performance in machine learning problems. So, we will use CNN as a classifier for image classification. So, the objective of this paper is that we will talk in detail about image classification in the following sections.

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