scholarly journals Classification and Generation of Microscopy Images With Plasmodium Falciparum Via Artificial Neural Networks

2020 ◽  
Author(s):  
Rija Tonny Christian Ramarolahy ◽  
Esther Opoku Gyasi ◽  
Alessandro Crimi
Keyword(s):  
Machine Learning ◽  
Blood Smear ◽  
Data Augmentation ◽  
Small Sample ◽  
Automated Detection ◽  
Machine Learning Techniques ◽  
Malaria Parasites ◽  
Learning Techniques ◽  
Synthetic Images ◽  
Microscopy Images

Abstract Background: Recent studies use machine-learning techniques to detect parasites in microscopy images automatically. However, these tools are trained and tested in specific datasets. Indeed, even if over-fitting is avoided during the improvements of computer vision applications, large differences are expected. Differences might be related to settings of camera (exposure, white balance settings, etc) and different blood film slides preparation. Moreover, generative adversial networks offer new opportunities in microscopy: data homogenization, and increase of images in case of imbalanced or small sample size. Methods: Taking into consideration all those aspects, in this paper, we describe a more complete view including both detection and generating synthetic images: i) an automated detection used to detect malaria parasites on stained blood smear images using machine learning techniques testing several datasets. ii) investigate transfer learning and further testing in different unseen datasets having different staining, microscope, resolution, etc. iii) a generative approach to create synthetic images which can deceive experts. Results: The tested architecture achieved 0.98 and 0.95 area under the ROC curve in classifying images with respectively thin and thick smear. Moreover, the generated images proved to be very similar to the original and difficult to be distinguished by an expert microscopist, which identified correcly the real data for one dataset but had 50\% misclassification for another dataset of images. Conclusion: The proposed deep-learning architecture performed well on a classification task for malaria parasites classification. The automated detection for malaria can help the technician to reduce their work and do not need any presence of experts. Moreover, generative networks can also be applied to blood smear images to generate useful images for microscopists. Opening new ways to data augmentation, translation and homogenization.

scholarly journals Classification and Generation of Microscopy Images with Plasmodium Falciparum via Artificial Neural Networks

2020 ◽  
Author(s):  
Rija Tonny Christian Ramarolahy ◽  
Esther Opoku Gyasi ◽  
Alessandro Crimi
Keyword(s):  
Machine Learning ◽  
Blood Smear ◽  
Data Augmentation ◽  
Small Sample ◽  
Automated Detection ◽  
Machine Learning Techniques ◽  
Malaria Parasites ◽  
Learning Techniques ◽  
Synthetic Images ◽  
Microscopy Images

AbstractBackgroundRecent studies use machine-learning techniques to detect parasites in microscopy images automatically. However, these tools are trained and tested in specific datasets. Indeed, even if over-fitting is avoided during the improvements of computer vision applications, large differences are expected. Differences might be related to settings of camera (exposure, white balance settings, etc) and different blood film slides preparation. Moreover, generative adversial networks offer new opportunities in microscopy: data homogenization, and increase of images in case of imbalanced or small sample size.MethodsTaking into consideration all those aspects, in this paper, we describe a more complete view including both detection and generating synthetic images: i) an automated detection used to detect malaria parasites on stained blood smear images using machine learning techniques testing several datasets. ii) investigate transfer learning and further testing in different unseen datasets having different staining, microscope, resolution, etc. iii) a generative approach to create synthetic images which can deceive experts.ResultsThe tested architecture achieved 0.98 and 0.95 area under the ROC curve in classifying images with respectively thin and thick smear. Moreover, the generated images proved to be very similar to the original and difficult to be distinguished by an expert microscopist, which identified correcly the real data for one dataset but had 50% misclassification for another dataset of images.ConclusionThe proposed deep-learning architecture performed well on a classification task for malaria parasites classification. The automated detection for malaria can help the technician to reduce their work and do not need any presence of experts. Moreover, generative networks can also be applied to blood smear images to generate useful images for microscopists. Opening new ways to data augmentation, translation and homogenization.

scholarly journals Training a deep learning model for single-cell segmentation without manual annotation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nizam Ud Din ◽  
Ji Yu
Keyword(s):  
Machine Learning ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Cell Segmentation ◽  
Learning Techniques ◽  
Signal Characteristics ◽  
Human Operators ◽  
Deep Learning Model ◽  
Microscopy Images ◽  
Segmentation Models

AbstractAdvances in the artificial neural network have made machine learning techniques increasingly more important in image analysis tasks. Recently, convolutional neural networks (CNN) have been applied to the problem of cell segmentation from microscopy images. However, previous methods used a supervised training paradigm in order to create an accurate segmentation model. This strategy requires a large amount of manually labeled cellular images, in which accurate segmentations at pixel level were produced by human operators. Generating training data is expensive and a major hindrance in the wider adoption of machine learning based methods for cell segmentation. Here we present an alternative strategy that trains CNNs without any human-labeled data. We show that our method is able to produce accurate segmentation models, and is applicable to both fluorescence and bright-field images, and requires little to no prior knowledge of the signal characteristics.

scholarly journals Predictive Modeling for Occupational Safety Outcomes and Days Away from Work Analysis in Mining Operations

2020 ◽  
Vol 17 (19) ◽  
pp. 7054
Author(s):  
Anurag Yedla ◽  
Fatemeh Davoudi Kakhki ◽  
Ali Jannesari
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Predictive Analytics ◽  
Machine Learning Algorithms ◽  
Mine Safety ◽  
Machine Learning Techniques ◽  
Health Administration ◽  
Tabular Data ◽  
Mining Operations ◽  
Learning Techniques

Mining is known to be one of the most hazardous occupations in the world. Many serious accidents have occurred worldwide over the years in mining. Although there have been efforts to create a safer work environment for miners, the number of accidents occurring at the mining sites is still significant. Machine learning techniques and predictive analytics are becoming one of the leading resources to create safer work environments in the manufacturing and construction industries. These techniques are leveraged to generate actionable insights to improve decision-making. A large amount of mining safety-related data are available, and machine learning algorithms can be used to analyze the data. The use of machine learning techniques can significantly benefit the mining industry. Decision tree, random forest, and artificial neural networks were implemented to analyze the outcomes of mining accidents. These machine learning models were also used to predict days away from work. An accidents dataset provided by the Mine Safety and Health Administration was used to train the models. The models were trained separately on tabular data and narratives. The use of a synthetic data augmentation technique using word embedding was also investigated to tackle the data imbalance problem. Performance of all the models was compared with the performance of the traditional logistic regression model. The results show that models trained on narratives performed better than the models trained on structured/tabular data in predicting the outcome of the accident. The higher predictive power of the models trained on narratives led to the conclusion that the narratives have additional information relevant to the outcome of injury compared to the tabular entries. The models trained on tabular data had a lower mean squared error compared to the models trained on narratives while predicting the days away from work. The results highlight the importance of predictors, like shift start time, accident time, and mining experience in predicting the days away from work. It was found that the F1 score of all the underrepresented classes except one improved after the use of the data augmentation technique. This approach gave greater insight into the factors influencing the outcome of the accident and days away from work.

scholarly journals Unsupervised deep learning method for cell segmentation

2021 ◽  
Author(s):  
Nizam Ud Din ◽  
Ji Yu
Keyword(s):  
Machine Learning ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Cell Segmentation ◽  
Learning Techniques ◽  
Signal Characteristics ◽  
Unsupervised Deep Learning ◽  
Human Operators ◽  
Microscopy Images ◽  
Segmentation Models

Advances in the artificial neural network have made machine learning techniques increasingly more important in image analysis tasks. More recently, convolutional neural networks (CNN) have been applied to the problem of cell segmentation from microscopy images. However, previous methods used a supervised training paradigm in order to create an accurate segmentation model. This strategy requires a large amount of manually labeled cellular images, in which accurate segmentations at pixel level were produced by human operators. Generating training data is expensive and a major hindrance in the wider adoption of machine learning based methods for cell segmentation. Here we present an alternative strategy that uses unsupervised learning to train CNNs without any human-labeled data. We show that our method is able to produce accurate segmentation models. More importantly, the algorithm is applicable to both fluorescence and bright-field images, requiring no prior knowledge of signal characteristics and requires no tuning of parameters.

scholarly journals Identifying At-Risk Online Learners by Psychological Variables Using Machine Learning Techniques

2020 ◽  
Vol 24 (4) ◽  
Author(s):  
Hsiang-yu Chien ◽  
Oi-Man Kwok ◽  
Yu-Chen Yeh ◽  
Noelle Wall Sweany ◽  
Eunkyeng Baek ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Online Courses ◽  
Small Sample ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Stepwise Logistic Regression ◽  
Online Learners ◽  
Psychological Variables ◽  
Learning Techniques

The purpose of this study was to investigate a predictive model of online learners’ learning outcomes through machine learning. To create a model, we observed students’ motivation, learning tendencies, online learning-motivated attention, and supportive learning behaviors along with final test scores. A total of 225 college students who were taking online courses participated. Longitudinal data were collected over three semesters (T1, T2, and T3). T3 was used as training data given that it contained the largest sample size across all three data waves. To analyze the data, two approaches were applied: (a) stepwise logistic regression and (b) random forest (RF). Results showed that RF used fewer items and predicted final grades more accurately in a small sample. Furthermore, it selected four items that might potentially be used to identify at-risk learners even before they enroll in an online course.

Automated Detection of Polycystic Ovary Syndrome Using Machine Learning Techniques

2021 ◽  
Author(s):  
Yasmine A. Abu Adla ◽  
Dalia G. Raydan ◽  
Mohammad-Zafer J. Charaf ◽  
Roua A. Saad ◽  
Jad Nasreddine ◽  
...  
Keyword(s):  
Machine Learning ◽  
Polycystic Ovary Syndrome ◽  
Polycystic Ovary ◽  
Automated Detection ◽  
Machine Learning Techniques ◽  
Ovary Syndrome ◽  
Learning Techniques
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