scholarly journals Six artificial intelligence paradigms for tissue characterisation and classification of non-COVID-19 pneumonia against COVID-19 pneumonia in computed tomography lungs

2021 ◽  
Author(s):  
Luca Saba ◽  
Mohit Agarwal ◽  
Anubhav Patrick ◽  
Anudeep Puvvula ◽  
Suneet K. Gupta ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computed Tomography ◽  
Tissue Characterisation

scholarly journals AI-Based Image Processing for COVID-19 Detection in Chest CT Scan Images

2021 ◽  
Vol 2 ◽  
Author(s):  
Hussein Kaheel ◽  
Ali Hussein ◽  
Ali Chehab
Keyword(s):  
Artificial Intelligence ◽  
Computed Tomography ◽  
Image Processing ◽  
Chest Ct ◽  
Levels Of Analysis ◽  
Training Phase ◽  
The Right ◽  
Best Fit ◽  
Chest Ct Scan

The COVID-19 pandemic has attracted the attention of big data analysts and artificial intelligence engineers. The classification of computed tomography (CT) chest images into normal or infected requires intensive data collection and an innovative architecture of AI modules. In this article, we propose a platform that covers several levels of analysis and classification of normal and abnormal aspects of COVID-19 by examining CT chest scan images. Specifically, the platform first augments the dataset to be used in the training phase based on a reliable collection of images, segmenting/detecting the suspicious regions in the images, and analyzing these regions in order to output the right classification. Furthermore, we combine AI algorithms, after choosing the best fit module for our study. Finally, we show the effectiveness of this architecture when compared to other techniques in the literature. The obtained results show that the accuracy of the proposed architecture is 95%.

Classification of Temporal Bone Pneumatization on Computed Tomography

Skull Base ◽  
2007 ◽  
Vol 16 (S 2) ◽  
Author(s):  
Su-Jin Han ◽  
Sang-Woo Moon ◽  
Mee-Hyun Song ◽  
Ho-Ki Lee
Keyword(s):  
Computed Tomography ◽  
Temporal Bone ◽  
Temporal Bone Pneumatization

Perspective approaches to classification of radiation accidents in radiology on the example of computed tomography

2021 ◽  
Author(s):  
A. V. Vodovatov ◽  
S. A. Ryzhov ◽  
L. A. Chipiga ◽  
A. M. Biblin ◽  
P. S. Druzhinina
Keyword(s):  
Computed Tomography ◽  
Radiation Accidents

scholarly journals Classification of artificial intelligence technologies to determine the civil liability

2021 ◽  
Vol 1794 (1) ◽  
pp. 012001
Author(s):  
A Alekseev ◽  
O Erakhtina ◽  
K Kondratyeva ◽  
T Nikitin
Keyword(s):  
Artificial Intelligence ◽  
Civil Liability

scholarly journals Micro Computed Tomography Analysis of Four-Way Conversion Catalysts using Artificial Intelligence-Enabled Image Processing

2021 ◽  
Vol 27 (S1) ◽  
pp. 1028-1029
Author(s):  
Robert Palomino ◽  
Ke-Bin Low ◽  
Chunxin Ji ◽  
Ivan Petrovic ◽  
Florian Waltz ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computed Tomography ◽  
Image Processing ◽  
Micro Computed Tomography ◽  
Tomography Analysis

scholarly journals Artificial Intelligence, 3D Documentation, and Rock Art—Approaching and Reflecting on the Automation of Identification and Classification of Rock Art Images

2021 ◽  
Author(s):  
Christian Horn ◽  
Oscar Ivarsson ◽  
Cecilia Lindhé ◽  
Rich Potter ◽  
Ashely Green ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Bronze Age ◽  
Data Augmentation ◽  
Rock Art ◽  
Training Data ◽  
Future Research ◽  
Rock Surface ◽  
Southern Scandinavia ◽  
Bounding Boxes

AbstractRock art carvings, which are best described as petroglyphs, were produced by removing parts of the rock surface to create a negative relief. This tradition was particularly strong during the Nordic Bronze Age (1700–550 BC) in southern Scandinavia with over 20,000 boats and thousands of humans, animals, wagons, etc. This vivid and highly engaging material provides quantitative data of high potential to understand Bronze Age social structures and ideologies. The ability to provide the technically best possible documentation and to automate identification and classification of images would help to take full advantage of the research potential of petroglyphs in southern Scandinavia and elsewhere. We, therefore, attempted to train a model that locates and classifies image objects using faster region-based convolutional neural network (Faster-RCNN) based on data produced by a novel method to improve visualizing the content of 3D documentations. A newly created layer of 3D rock art documentation provides the best data currently available and has reduced inscribed bias compared to older methods. Several models were trained based on input images annotated with bounding boxes produced with different parameters to find the best solution. The data included 4305 individual images in 408 scans of rock art sites. To enhance the models and enrich the training data, we used data augmentation and transfer learning. The successful models perform exceptionally well on boats and circles, as well as with human figures and wheels. This work was an interdisciplinary undertaking which led to important reflections about archaeology, digital humanities, and artificial intelligence. The reflections and the success represented by the trained models open novel avenues for future research on rock art.

scholarly journals Detection and Severity Classification of COVID-19 in CT Images Using Deep Learning

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 893
Author(s):  
Yazan Qiblawey ◽  
Anas Tahir ◽  
Muhammad E. H. Chowdhury ◽  
Amith Khandakar ◽  
Serkan Kiranyaz ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Early Stage ◽  
Similarity Coefficient ◽  
Dice Similarity Coefficient ◽  
Lung Region ◽  
Computed Tomography Images ◽  
Feature Pyramid ◽  
Severity Levels

Detecting COVID-19 at an early stage is essential to reduce the mortality risk of the patients. In this study, a cascaded system is proposed to segment the lung, detect, localize, and quantify COVID-19 infections from computed tomography images. An extensive set of experiments were performed using Encoder–Decoder Convolutional Neural Networks (ED-CNNs), UNet, and Feature Pyramid Network (FPN), with different backbone (encoder) structures using the variants of DenseNet and ResNet. The conducted experiments for lung region segmentation showed a Dice Similarity Coefficient (DSC) of 97.19% and Intersection over Union (IoU) of 95.10% using U-Net model with the DenseNet 161 encoder. Furthermore, the proposed system achieved an elegant performance for COVID-19 infection segmentation with a DSC of 94.13% and IoU of 91.85% using the FPN with DenseNet201 encoder. The proposed system can reliably localize infections of various shapes and sizes, especially small infection regions, which are rarely considered in recent studies. Moreover, the proposed system achieved high COVID-19 detection performance with 99.64% sensitivity and 98.72% specificity. Finally, the system was able to discriminate between different severity levels of COVID-19 infection over a dataset of 1110 subjects with sensitivity values of 98.3%, 71.2%, 77.8%, and 100% for mild, moderate, severe, and critical, respectively.

scholarly journals Convolutional Neural Network-Based Artificial Intelligence for Classification of Protein Localization Patterns

Biomolecules ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 264
Author(s):  
Kaisa Liimatainen ◽  
Riku Huttunen ◽  
Leena Latonen ◽  
Pekka Ruusuvuori
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Convolutional Neural Network ◽  
High Throughput ◽  
Protein Function ◽  
Protein Localization ◽  
Convolutional Network ◽  
High Throughput Analysis ◽  
Fully Convolutional Network

Identifying localization of proteins and their specific subpopulations associated with certain cellular compartments is crucial for understanding protein function and interactions with other macromolecules. Fluorescence microscopy is a powerful method to assess protein localizations, with increasing demand of automated high throughput analysis methods to supplement the technical advancements in high throughput imaging. Here, we study the applicability of deep neural network-based artificial intelligence in classification of protein localization in 13 cellular subcompartments. We use deep learning-based on convolutional neural network and fully convolutional network with similar architectures for the classification task, aiming at achieving accurate classification, but importantly, also comparison of the networks. Our results show that both types of convolutional neural networks perform well in protein localization classification tasks for major cellular organelles. Yet, in this study, the fully convolutional network outperforms the convolutional neural network in classification of images with multiple simultaneous protein localizations. We find that the fully convolutional network, using output visualizing the identified localizations, is a very useful tool for systematic protein localization assessment.

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