scholarly journals A Machine Learning-Based System for Detecting Leishmaniasis in Microscopic Images

2021 ◽  
Author(s):  
Mojtaba Zare ◽  
Hossein Akbarialiabad ◽  
Hossein Parsaei ◽  
Qasem Asgari ◽  
Ali Alinejad ◽  
...  
Keyword(s):  
Processing Speed ◽  
Visual Detection ◽  
High Sensitivity ◽  
Leishmania Parasite ◽  
Parasitic Disease ◽  
Integral Image ◽  
Viable Option ◽  
High Processing Speed ◽  
Image Creation ◽  
High Processing

Abstract Background: Leishmaniasis, a disease caused by a protozoan, causes numerous deaths in humans each year. After malaria, Leishmaniasis is known to be the deadliest parasitic disease globally. Currently, direct visual detection of Leishmania parasite through microscopy is the “gold standard” for the diagnosis of this disease. However, this method is time-consuming and subject to errors. This study was aimed to develop an artificial intelligence-based algorithm and image processing algorithms for the automatic diagnosis of Leishmaniasis.Methods: The Viola-Jones algorithm was used in this study due to its high recognition speed. This algorithm performs in four stages: detection of Haar-like features, integral image creation, Adaboost training, cascade architecture.Results: A 65% recall and 83% precision was concluded in the detection of macrophages infected with the Leishmania parasite. Also, these numbers were 52% and 35%, respectively, related to amastigotes outside of macrophages.Conclusion: The results contain a fairly high sensitivity, with the specificity being less satisfactory. High processing speed, ease of work, and low expenses are advantages of the presented method compared to other procedures. By adding a few adjustments, this method could be considered a viable option.

scholarly journals A machine learning-based system for detecting leishmaniasis in microscopic images

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Mojtaba Zare ◽  
Hossein Akbarialiabad ◽  
Hossein Parsaei ◽  
Qasem Asgari ◽  
Ali Alinejad ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Visual Detection ◽  
Detection System ◽  
Cost Effective ◽  
Leishmania Parasite ◽  
Parasitic Disease ◽  
Integral Image ◽  
Parasite Detection ◽  
Image Creation

Abstract Background Leishmaniasis, a disease caused by a protozoan, causes numerous deaths in humans each year. After malaria, leishmaniasis is known to be the deadliest parasitic disease globally. Direct visual detection of leishmania parasite through microscopy is the frequent method for diagnosis of this disease. However, this method is time-consuming and subject to errors. This study was aimed to develop an artificial intelligence-based algorithm for automatic diagnosis of leishmaniasis. Methods We used the Viola-Jones algorithm to develop a leishmania parasite detection system. The algorithm includes three procedures: feature extraction, integral image creation, and classification. Haar-like features are used as features. An integral image was used to represent an abstract of the image that significantly speeds up the algorithm. The adaBoost technique was used to select the discriminate features and to train the classifier. Results A 65% recall and 50% precision was concluded in the detection of macrophages infected with the leishmania parasite. Also, these numbers were 52% and 71%, respectively, related to amastigotes outside of macrophages. Conclusion The developed system is accurate, fast, easy to use, and cost-effective. Therefore, artificial intelligence might be used as an alternative for the current leishmanial diagnosis methods.

scholarly journals An Analysis of the Optical System of a Length Measurement Comparator

Mechanika ◽  
2020 ◽  
Vol 26 (6) ◽  
pp. 518-525
Author(s):  
Rimantas LAZDINAS ◽  
Mindaugas JUREVICIUS ◽  
Vladas VEKTERIS ◽  
Vytautas TURLA ◽  
Eugenijus JURKONIS
Keyword(s):  
Optical System ◽  
Processing Speed ◽  
Length Measurement ◽  
Identification Algorithm ◽  
High Processing Speed ◽  
Image Blurring ◽  
Scale Calibration ◽  
Calibration Accuracy ◽  
The Impact ◽  
High Processing

In the paper, the optical system of a precise bar length measuring comparator is analysed. The requirements for such a system are determined and systematized. The impact of the resolution, signal discretization frequency, image blurring, bar edge measurement indeterminacy and camera noise on measuring the bar width and establishing the bar position is discussed upon. Various algorithms have been analysed and finally a bar edge identification algorithm oriented to the scale calibration accuracy and the high processing speed was proposed. In the end of the paper, conclusions are provided.

Flour Bag Counting System Based on Machine Vision

2012 ◽  
Vol 562-564 ◽  
pp. 1625-1629 ◽  
Author(s):  
Chun Xia Ma ◽  
Wen He Song ◽  
Jun Wu ◽  
Yue Wang ◽  
Zhi Tao Xiao
Keyword(s):  
Processing Speed ◽  
Counting Process ◽  
Plant Production ◽  
Detection Accuracy ◽  
Test Results ◽  
Counting System ◽  
High Processing Speed ◽  
Flour Production ◽  
High Processing ◽  
Time Acquisition

According to the specific characteristics of Flour Plant production line, the bag counting system for Flour Plant based on Daheng Image acquisition card DH-CG410 and Microsoft VC++ 2005 platform is designed. Using digital image processing technology, the system can be used to detect flour bag by analyzing real-time acquisition video of the flour bag. So it is available for flour production counting. Test results show that the system has the advantages of stable working state, high detection accuracy, high processing speed. It can systematize and standardize the Flour Plant flour bag counting process.

Application of Unpiloted Aircraft in Land Resource Management of Sichuan Province

2012 ◽  
Vol 562-564 ◽  
pp. 1732-1737
Author(s):  
Si Cheng ◽  
Fei Yu Chen ◽  
Yun Sheng Wang
Keyword(s):  
Resource Management ◽  
Processing Speed ◽  
Low Cost ◽  
Sichuan Province ◽  
Land Resource ◽  
High Definition ◽  
Resource Protection ◽  
High Processing Speed ◽  
Aircraft System ◽  
High Processing

For defects of traditional aviation photography and remote sensing satellite, developing unpiloted aircraft technology for land resource management in Sichuan Province is essential for land resource administration. Unpiloted aircraft technology is a development of traditional aviation photography, which wipes out many shortages of those technologies. It has features of low cost, easy control, quick responsiveness, high processing speed, and capacity of taking high definition aviation photography. This article focuses on features, system organization, and unsolved problems of unpiloted aircraft photography system. The research team has to use knowledge in many categories to design an unpiloted aircraft system, such as artificial intelligence, aviation, topography, and information technology. Moreover, this article introduces applications of this technology in land resource management, geo-hazard prevention, mineral resource protection, and many other orientations in Sichuan Province. In addition the perspective of using unpiloted aircraft in geo-hazard prevention is analyzed and some ideal results are also introduced.

Network Communication System Based on AT91SAM92 Hardware Design

2011 ◽  
Vol 55-57 ◽  
pp. 614-617
Author(s):  
Dong Yun Luo ◽  
You Mu Zhang
Keyword(s):  
Processing Speed ◽  
Communication System ◽  
Modular Design ◽  
Hardware Design ◽  
Design Concept ◽  
Network Communication ◽  
High Processing Speed ◽  
High Processing

The network communication system is presented for controller of AT91SAM9261 architecture which is based on ARM926EJ-S core, modular design concept is given by the each har­d­ware related module and driver design. High processing speed of the system on the basis of this system can be extended .

ALGORITHMIC DECOMPOSITION AND REDUCTION OF LARGE MATHEMATICAL MODELS

2019 ◽  
Author(s):  
Rogoza W. ◽  
Ischenko A.
Keyword(s):  
Processing Speed ◽  
Large Data ◽  
Data Models ◽  
Software Developers ◽  
High Processing Speed ◽  
Special Software ◽  
Complex Engineering ◽  
Mathematical Equations ◽  
Mapreduce Model ◽  
High Processing

The problems associated with the processing of large amounts of data, initiated research in the field of creating special software that allows us to process this data online. A well-known example of such software is the MapReduce computational model developed and implemented by Google. The advantages of MapReduce are the high processing speed of large data arrays, achieved through data decomposition and reduction, as well as the ability to implement this model on standard hardware. Creating algorithms and programs that comply with the principles of the MapReduce model, depends on the specifics of the tasks that are solved, and relies on the software developers. Most of the algorithms known today are designed to process large arrays of data coming to a computer system online without changing data models (i.e. the data is processed as it enters the system in the data stream). At the same time, it is possible to distinguish classes of tasks for which the data on the objects under study are redundant, and their volume can be significantly reduced even before this data is available for their transformation. As is shown in the article, this class includes the tasks of mathematical simulation of complex engineering objects, the data models of which are represented in the form of mathematical equations that describe the physical states of the objects. The authors discuss the problems of decomposition and reduction of models at the level of transformations of the mentioned mathematical equations, which is why the authors call this approach algorithmic decomposition and reduction.

FQC: A novel approach for efficient compression, archival, and dissemination of fastq datasets

2015 ◽  
Vol 13 (03) ◽  
pp. 1541003 ◽  
Author(s):  
Anirban Dutta ◽  
Mohammed Monzoorul Haque ◽  
Tungadri Bose ◽  
C. V. S. K. Reddy ◽  
Sharmila S. Mande
Keyword(s):  
Processing Speed ◽  
Sequence Data ◽  
Compression Method ◽  
Data Repositories ◽  
Compression Efficiency ◽  
Compression Algorithms ◽  
Novel Approach ◽  
High Processing Speed ◽  
High Processing ◽  
Efficiency Data

Sequence data repositories archive and disseminate fastq data in compressed format. In spite of having relatively lower compression efficiency, data repositories continue to prefer GZIP over available specialized fastq compression algorithms. Ease of deployment, high processing speed and portability are the reasons for this preference. This study presents FQC, a fastq compression method that, in addition to providing significantly higher compression gains over GZIP, incorporates features necessary for universal adoption by data repositories/end-users. This study also proposes a novel archival strategy which allows sequence repositories to simultaneously store and disseminate lossless as well as (multiple) lossy variants of fastq files, without necessitating any additional storage requirements. For academic users, Linux, Windows, and Mac implementations (both 32 and 64-bit) of FQC are freely available for download at: https://metagenomics.atc.tcs.com/compression/FQC .

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