scholarly journals Detection of Malaria Using Machine Learning

2021 ◽  
pp. 401-408
Author(s):  
Prof. S. T. Sawale ◽  
Apeksha P. Mahajan ◽  
Shital B. Borle
Keyword(s):  
Machine Learning ◽  
Virus Infection ◽  
Blood Smear ◽  
Blood Transfusions ◽  
Malaria Parasites ◽  
Traditional Methods ◽  
Contaminated Blood

“Malaria is a blood-borne disease caused by Plasmodium-borne diseases. Common methods of detecting malaria include preparing a blood smear and examining the contaminated blood smear using a microscope to detect Plasmodium virus infection, which relies heavily on the techniques studied. At the bottom of this paper, with the aim of discriminating against malaria parasites, shallow study tools are used against traditional methods, which have other pitfalls related to understanding and disunity. The described method determines the spread of malaria with the help of photographs taken of patients without blood transfusions or the need for specialists.

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 Malaria parasite detection in thick blood smear microscopic images using modified YOLOV3 and YOLOV4 models

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Fetulhak Abdurahman ◽  
Kinde Anlay Fante ◽  
Mohammed Aliy
Keyword(s):  
Object Detection ◽  
Malaria Parasite ◽  
Blood Smear ◽  
Clustering Algorithm ◽  
State Of The Art ◽  
Detection Accuracy ◽  
Small Object ◽  
Thick Blood Smear ◽  
Malaria Parasites ◽  
Microscopic Images

Abstract Background Manual microscopic examination of Leishman/Giemsa stained thin and thick blood smear is still the “gold standard” for malaria diagnosis. One of the drawbacks of this method is that its accuracy, consistency, and diagnosis speed depend on microscopists’ diagnostic and technical skills. It is difficult to get highly skilled microscopists in remote areas of developing countries. To alleviate this problem, in this paper, we propose to investigate state-of-the-art one-stage and two-stage object detection algorithms for automated malaria parasite screening from microscopic image of thick blood slides. Results YOLOV3 and YOLOV4 models, which are state-of-the-art object detectors in accuracy and speed, are not optimized for detecting small objects such as malaria parasites in microscopic images. We modify these models by increasing feature scale and adding more detection layers to enhance their capability of detecting small objects without notably decreasing detection speed. We propose one modified YOLOV4 model, called YOLOV4-MOD and two modified models of YOLOV3, which are called YOLOV3-MOD1 and YOLOV3-MOD2. Besides, new anchor box sizes are generated using K-means clustering algorithm to exploit the potential of these models in small object detection. The performance of the modified YOLOV3 and YOLOV4 models were evaluated on a publicly available malaria dataset. These models have achieved state-of-the-art accuracy by exceeding performance of their original versions, Faster R-CNN, and SSD in terms of mean average precision (mAP), recall, precision, F1 score, and average IOU. YOLOV4-MOD has achieved the best detection accuracy among all the other models with a mAP of 96.32%. YOLOV3-MOD2 and YOLOV3-MOD1 have achieved mAP of 96.14% and 95.46%, respectively. Conclusions The experimental results of this study demonstrate that performance of modified YOLOV3 and YOLOV4 models are highly promising for detecting malaria parasites from images captured by a smartphone camera over the microscope eyepiece. The proposed system is suitable for deployment in low-resource setting areas.

Machine Learning Arrives in Archaeology

2021 ◽  
Vol 9 (2) ◽  
pp. 186-191
Author(s):  
Simon H. Bickler
Keyword(s):  
Machine Learning ◽  
Rapid Development ◽  
Rapid Identification ◽  
First Century ◽  
Traditional Methods ◽  
Archaeological Practice ◽  
The World ◽  
Archaeological Work ◽  
Archaeological Analysis

OverviewMachine learning (ML) is rapidly being adopted by archaeologists interested in analyzing a range of geospatial, material cultural, textual, natural, and artistic data. The algorithms are particularly suited toward rapid identification and classification of archaeological features and objects. The results of these new studies include identification of many new sites around the world and improved classification of large archaeological datasets. ML fits well with more traditional methods used in archaeological analysis, and it remains subject to both the benefits and difficulties of those approaches. Small datasets associated with archaeological work make ML vulnerable to hidden complexity, systemic bias, and high validation costs if not managed appropriately. ML's scalability, flexibility, and rapid development, however, make it an essential part of twenty-first-century archaeological practice. This review briefly describes what ML is, how it is being used in archaeology today, and where it might be used in the future for archaeological purposes.

Machine Learning Techniques for IoT-Based Indoor Tracking and Localization

2022 ◽  
pp. 123-145
Author(s):  
Pelin Yildirim Taser ◽  
Vahid Khalilpour Akram
Keyword(s):  
Machine Learning ◽  
Indoor Localization ◽  
Learning Algorithms ◽  
Distance Estimation ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Traditional Methods ◽  
Learning Techniques ◽  
Localization Systems

The GPS signals are not available inside the buildings; hence, indoor localization systems rely on indoor technologies such as Bluetooth, WiFi, and RFID. These signals are used for estimating the distance between a target and available reference points. By combining the estimated distances, the location of the target nodes is determined. The wide spreading of the internet and the exponential increase in small hardware diversity allow the creation of the internet of things (IoT)-based indoor localization systems. This chapter reviews the traditional and machine learning-based methods for IoT-based positioning systems. The traditional methods include various distance estimation and localization approaches; however, these approaches have some limitations. Because of the high prediction performance, machine learning algorithms are used for indoor localization problems in recent years. The chapter focuses on presenting an overview of the application of machine learning algorithms in indoor localization problems where the traditional methods remain incapable.

Solid-State Nanopore Platform Integrated with Machine Learning for Digital Diagnosis of Virus Infection

2020 ◽  
Author(s):  
Akihide Arima ◽  
Makusu Tsutsui ◽  
Takashi Washio ◽  
Yoshinobu Baba ◽  
Tomoji Kawai
Keyword(s):  
Machine Learning ◽  
Solid State ◽  
Virus Infection

Twitter sentiment analysis for the estimation of voting intention in the 2017 Chilean elections

2020 ◽  
Vol 24 (5) ◽  
pp. 1141-1160
Author(s):  
Tomás Alegre Sepúlveda ◽  
Brian Keith Norambuena
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Sentiment Analysis ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Traditional Methods ◽  
Actual Result ◽  
Learning Techniques ◽  
Vector Machines

In this paper, we apply sentiment analysis methods in the context of the first round of the 2017 Chilean elections. The purpose of this work is to estimate the voting intention associated with each candidate in order to contrast this with the results from classical methods (e.g., polls and surveys). The data are collected from Twitter, because of its high usage in Chile and in the sentiment analysis literature. We obtained tweets associated with the three main candidates: Sebastián Piñera (SP), Alejandro Guillier (AG) and Beatriz Sánchez (BS). For each candidate, we estimated the voting intention and compared it to the traditional methods. To do this, we first acquired the data and labeled the tweets as positive or negative. Afterward, we built a model using machine learning techniques. The classification model had an accuracy of 76.45% using support vector machines, which yielded the best model for our case. Finally, we use a formula to estimate the voting intention from the number of positive and negative tweets for each candidate. For the last period, we obtained a voting intention of 35.84% for SP, compared to a range of 34–44% according to traditional polls and 36% in the actual elections. For AG we obtained an estimate of 37%, compared with a range of 15.40% to 30.00% for traditional polls and 20.27% in the elections. For BS we obtained an estimate of 27.77%, compared with the range of 8.50% to 11.00% given by traditional polls and an actual result of 22.70% in the elections. These results are promising, in some cases providing an estimate closer to reality than traditional polls. Some differences can be explained due to the fact that some candidates have been omitted, even though they held a significant number of votes.

scholarly journals Repurposing Quinacrine against Ebola Virus Infection In Vivo

2019 ◽  
Vol 63 (9) ◽  
Author(s):  
Thomas R. Lane ◽  
Jason E. Comer ◽  
Alexander N. Freiberg ◽  
Peter B. Madrid ◽  
Sean Ekins
Keyword(s):  
Machine Learning ◽  
Virus Infection ◽  
Ebola Virus ◽  
Lethal Challenge ◽  
Once Daily ◽  
Machine Learning Model ◽  
Orally Bioavailable ◽  
Ebola Virus Infection

ABSTRACT Quinacrine hydrochloride is a small-molecule, orally bioavailable drug that has been used clinically as an antimalarial and for many other applications. A machine learning model trained on Ebola virus (EBOV) screening data identified quinacrine as a potent (nanomolar) in vitro inhibitor. In the current study, quinacrine 25 mg/kg was shown to protect 70% of mice (statistically significant) from a lethal challenge with mouse-adapted EBOV with once-daily intraperitoneal dosing for 8 days.

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