Artificial Intelligence for the Identification of Endometrial Malignancies

2020 ◽  
pp. 266-279
Author(s):  
Abraham Pouliakis ◽  
Niki Margari ◽  
Effrosyni Karakitsou ◽  
Evangelia Alamanou ◽  
Nikolaos Koureas ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Image Analysis ◽  
Vector Quantization ◽  
System Performance ◽  
Morphometric Characteristics ◽  
Cell Nuclei ◽  
Performance Indices ◽  
And Performance

Aim of this article is to investigate the potential of Artificial Intelligence (AI) in the discrimination between benign and malignant endometrial nuclei and lesions. For this purpose, 416 histologically confirmed liquid-based cytological smears were collected and morphometric characteristics of cell nuclei were measured via image analysis. Then, 50% of the cases were used to train an AI system, specifically a learning vector quantization (LVQ) neural network. As a result, cell nuclei were classified as benign or malignant. Data from the remaining 50% of the cases were used to evaluate the AI system performance. By nucleic classification, an algorithm for the classification of individual patients was constructed, and performance indices on patient classification were calculated. The sensitivity for the classification of nuclei was 77.95%, and the specificity was 73.93%. For the classification of individual patients, the sensitivity was 90.70% and the specificity 82.79%. These results indicate that an AI system can have an important role in endometrial lesions classification.

Artificial Intelligence for the Identification of Endometrial Malignancies

2018 ◽  
Vol 7 (2) ◽  
pp. 37-50 ◽  
Author(s):  
Abraham Pouliakis ◽  
Niki Margari ◽  
Effrosyni Karakitsou ◽  
Evangelia Alamanou ◽  
Nikolaos Koureas ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Vector Quantization ◽  
System Performance ◽  
Patient Classification ◽  
Morphometric Characteristics ◽  
Cell Nuclei ◽  
Performance Indices ◽  
And Performance

Aim of this article is to investigate the potential of Artificial Intelligence (AI) in the discrimination between benign and malignant endometrial nuclei and lesions. For this purpose, 416 histologically confirmed liquid-based cytological smears were collected and morphometric characteristics of cell nuclei were measured via image analysis. Then, 50% of the cases were used to train an AI system, specifically a learning vector quantization (LVQ) neural network. As a result, cell nuclei were classified as benign or malignant. Data from the remaining 50% of the cases were used to evaluate the AI system performance. By nucleic classification, an algorithm for the classification of individual patients was constructed, and performance indices on patient classification were calculated. The sensitivity for the classification of nuclei was 77.95%, and the specificity was 73.93%. For the classification of individual patients, the sensitivity was 90.70% and the specificity 82.79%. These results indicate that an AI system can have an important role in endometrial lesions classification.

Artificial Intelligence and Image Analysis for the Identification of Endometrial Malignancies

2020 ◽  
pp. 110-146
Author(s):  
Abraham Pouliakis ◽  
Vasileia Damaskou ◽  
Niki Margari ◽  
Efrossyni Karakitsou ◽  
Vasilios Pergialiotis ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Analysis ◽  
Vector Quantization ◽  
Machine Learning Algorithms ◽  
Cell Nuclei ◽  
Liquid Based Cytology ◽  
Artificial Neural Network Ann

The aim of this study is to compare machine learning algorithms (MLAs) in the discrimination between benign and malignant endometrial nuclei and lesions. Nuclei characteristics are obtained via image analysis and were measured from liquid-based cytology slides. Four hundred sixteen histologically confirmed patients were involved, 168 healthy, and the remaining with pathological endometrium. Fifty percent of the cases were used to three MLAs: a feedforward artificial neural network (ANN) trained by the backpropagation algorithm, a learning vector quantization (LVQ), and a competitive learning ANN. The outcome of this process was the classification of cell nuclei as benign or malignant. Based on the nuclei classification, an algorithm to classify individual patients was constructed. The sensitivity of the MLAs in training set for nuclei classification was in the range of 77%-84%. Patients' classification had sensitivity in the range of 90%-98%. These findings indicate that MLAs have good performance for the classification of endometrial nuclei and lesions.

Artificial Intelligence via Competitive Learning and Image Analysis for Endometrial Malignancies

2019 ◽  
Vol 8 (4) ◽  
pp. 38-54 ◽  
Author(s):  
Abraham Pouliakis ◽  
Niki Margari ◽  
Effrosyni Karakitsou ◽  
George Valasoulis ◽  
Nektarios Koufopoulos ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
System Performance ◽  
Malignant Disease ◽  
Competitive Learning ◽  
Image Analysis System ◽  
Morphometric Features ◽  
Analysis System ◽  
Histological Confirmation

Objective of this study is to investigate the potential of an artificial intelligence (AI) technique, based on competitive learning, for the discrimination of benign from malignant endometrial nuclei and lesions. For this purpose, 416 liquid-based cytological smears with histological confirmation were collected, each smear corresponded to one patient. From each smear was extracted nuclear morphometric features by the application of an image analysis system. Subsequently nuclei measurement from 50% of the cases were used to train the AI system to classify each individual nucleus as benign or malignant. The remaining measurement, from the unused 50% of the cases, were used for AI system performance evaluation. Based on the results of nucleus classification the patients were discriminated as having benign or malignant disease by a secondary subsystem specifically trained for this purpose. Based on the results it was conclude that AI based computerized systems have the potential for the classification of both endometrial nuclei and lesions.

scholarly journals Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2648
Author(s):  
Muhammad Aamir ◽  
Tariq Ali ◽  
Muhammad Irfan ◽  
Ahmad Shaf ◽  
Muhammad Zeeshan Azam ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Natural Disasters ◽  
Deep Convolutional Neural Network ◽  
Multispectral Images ◽  
Learning Techniques ◽  
Proposed Model ◽  
Disaster Intensity ◽  
And Performance

Natural disasters not only disturb the human ecological system but also destroy the properties and critical infrastructures of human societies and even lead to permanent change in the ecosystem. Disaster can be caused by naturally occurring events such as earthquakes, cyclones, floods, and wildfires. Many deep learning techniques have been applied by various researchers to detect and classify natural disasters to overcome losses in ecosystems, but detection of natural disasters still faces issues due to the complex and imbalanced structures of images. To tackle this problem, we propose a multilayered deep convolutional neural network. The proposed model works in two blocks: Block-I convolutional neural network (B-I CNN), for detection and occurrence of disasters, and Block-II convolutional neural network (B-II CNN), for classification of natural disaster intensity types with different filters and parameters. The model is tested on 4428 natural images and performance is calculated and expressed as different statistical values: sensitivity (SE), 97.54%; specificity (SP), 98.22%; accuracy rate (AR), 99.92%; precision (PRE), 97.79%; and F1-score (F1), 97.97%. The overall accuracy for the whole model is 99.92%, which is competitive and comparable with state-of-the-art algorithms.

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.

Decision support or autonomous artificial intelligence? The case of wrong blood in tube errors

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Christopher-John L. Farrell
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Computer Simulation ◽  
Clinical Chemistry ◽  
Human Interaction ◽  
Final Decision ◽  
P Values ◽  
Laboratory Staff

Abstract Objectives Artificial intelligence (AI) models are increasingly being developed for clinical chemistry applications, however, it is not understood whether human interaction with the models, which may occur once they are implemented, improves or worsens their performance. This study examined the effect of human supervision on an artificial neural network trained to identify wrong blood in tube (WBIT) errors. Methods De-identified patient data for current and previous (within seven days) electrolytes, urea and creatinine (EUC) results were used in the computer simulation of WBIT errors at a rate of 50%. Laboratory staff volunteers reviewed the AI model’s predictions, and the EUC results on which they were based, before making a final decision regarding the presence or absence of a WBIT error. The performance of this approach was compared to the performance of the AI model operating without human supervision. Results Laboratory staff supervised the classification of 510 sets of EUC results. This workflow identified WBIT errors with an accuracy of 81.2%, sensitivity of 73.7% and specificity of 88.6%. However, the AI model classifying these samples autonomously was superior on all metrics (p-values<0.05), including accuracy (92.5%), sensitivity (90.6%) and specificity (94.5%). Conclusions Human interaction with AI models can significantly alter their performance. For computationally complex tasks such as WBIT error identification, best performance may be achieved by autonomously functioning AI models.

Image Recognition Technology Based on Artificial Intelligence

2013 ◽  
Vol 347-350 ◽  
pp. 3537-3540
Author(s):  
Hai Yun Lin ◽  
Yu Jiao Wang ◽  
Jian Chun Cai
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Semantic Feature ◽  
Current Image ◽  
Semantic Feature Extraction

In respect of the classification of current image retrieval technology and the existing issues, the paper put forward a method designed for image semantic feature extraction based on artificial intelligence. The new method has solved the tough problem of image semantic feature extraction, by fusing fuzzy logic, genetic algorithm and artificial neural network altogether, which greatly improved the efficiency and accuracy of image retrieval.

Fast Neuro-Classification of New and Used Bills Using Spectral Patterns of Acoustic Data

2000 ◽  
Vol 4 (1) ◽  
pp. 12-17
Author(s):  
Dongshik Kang ◽  
◽  
Sigeru Omatu ◽  
Michifumi Yoshioka
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Vector Quantization ◽  
Digital Filters ◽  
Spectral Pattern ◽  
Competitive Neural Network ◽  
Acoustic Data ◽  
Adaptive Digital Filters ◽  
Spectral Patterns

An advanced neuro-classification of new and used bills using the spectral patterns is proposed. An acoustic spectral pattern is obtained from the output of the two-stage adaptive digital filters (ADFs) for time-series acoustic data. The acoustic spectral patterns are fed to a competitive neural network, and classified into some categories which show worn-out degrees of the bill. The proposed method is based on extension of an ADF, an individual adaptation (IA) algorithm, and a learning vector quantization (LVQ) algorithm. The experimental results show that the proposed method is useful to classify new and used bills.

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