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1 |
A deep learning approach to the screening of malaria infection: Automated and rapid cell counting, object detection and instance segmentation using Mask R-CNN
یک روش یادگیری عمیق برای غربالگری عفونت مالاریا: شمارش خودکار و سریع سلول ها ، تشخیص اشیاء و تقسیم بندی نمونه با استفاده از Mask R-CNN-2021 Accurate and early diagnosis is critical to proper malaria treatment and hence death prevention. Several com- puter vision technologies have emerged in recent years as alternatives to traditional microscopy and rapid diagnostic tests. In this work, we used a deep learning model called Mask R-CNN that is trained on uninfected and Plasmodium falciparum-infected red blood cells. Our predictive model produced reports at a rate 15 times faster than manual counting without compromising on accuracy. Another unique feature of our model is its ability to generate segmentation masks on top of bounding box classifications for immediate visualization, making it superior to existing models. Furthermore, with greater standardization, it holds much potential to reduce errors arising from manual counting and save a significant amount of human resources, time, and cost. Keywords: Malaria diagnosis | Mask R-CNN | Computer vision | Image analysis |
مقاله انگلیسی |
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The development of complex and controversial innovations. Genetically modified mosquitoes for malaria eradication
توسعه نوآوری های پیچیده و بحث برانگیز. پشه های اصلاح شده ژنتیکی برای ریشه کن کردن مالاریا-2020 When there is significant uncertainty in an innovation project, research literature suggests that strictly sequencing
actions and stages may not be an appropriate mode of project management. We use a longitudinal
process approach and qualitative system dynamics modelling to study the development of genetically modified
(GM) mosquitoes for malaria eradication in an African country. Our data were collected in real time, from early
scientific research to deployment of the first prototype mosquitoes in the field. The gene drive technology for
modifying the mosquitoes is highly complex and controversial due to risks associated with its characteristics as a
living, self-replicating technology. We show that in this case the innovation journey is linear and highly
structured, but also embedded within a wider system of adoption that displays emergent behaviour. Although
the need to control risks associated with the technology imposes a linearity to the NPD process, there are
possibilities for deviation from a more structured sequence of stages. This arises from the effects of feedback
loops in the wider system of evidence creation and learning at the population and governance levels, which
cumulatively impact on acceptance of the innovation. The NPD and adoption processes are therefore closely
intertwined, meaning that the endpoint for R&D and beginning of mainstream adoption and diffusion are unclear.
A key challenge for those responsible for NPD and its regulation is to plan for the adoption of the technology
while simultaneously conducting its scientific and technical development. Keywords: New product development | Adoption | Genetically modified mosquitoes | Living technology | Gene drive | Malaria |
مقاله انگلیسی |
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Artificial neural network based prediction of malaria abundances using big data: A knowledge capturing approach
پیشگیری از فراوانی مالاریا بر اساس شبکه عصبی مصنوعی با استفاده از داده های بزرگ: رویکرد جذب دانش-2018 Background and objective: Malaria is one of the most prevalent diseases in urban areas. Malaria flourishes in subtropical countries and affect the public health. The impact is very high, where health monitoring facilities are
very limited. To minimize the impact of malaria population in sub-tropical domains, a suitable disease prediction
model is required. The objective of this study is to determine the malaria abundances using clinical and environmental variables with Big Data on the geographical location of Khammam district, Telanagana, India.
Methods: Prediction model is based on the data collected from primary health centres of department of vector
borne diseases (DVBD) of Khammam district and satellite data such as rain fall, relative humidity, temperature and
vegetation taken for the time period of 1995–2014. In this study, we test the efficacy of the artificial neural
network (ANN) for mosquito abundance prediction. Prediction model was developed for the period of 2015
using a feed forward neural network and compared with the observed values.
Results and conclusions: The results vary from area to area based on clinical variables and rainfall in the prediction model corresponding to areas. The average error of the prediction model ranges from 18% to 117%.
Clinical data such as number of patients treated with symptoms and without symptoms can improve the prediction level when combined with environmental variables. We perform preliminary findings of malaria
abundances by collecting clinical big data across different seasons. Further, more exploration is required in
prediction of malaria using big data to improve the accuracy in real practice. In this manuscript, we perform
some preliminary findings of malaria abundances by collecting larger data across different seasons. Till today,
many models have been developed to examine the malaria prediction with different approaches, but malaria
prediction with environmental and clinical data is a new approach with big data analysis.
Keywords: Malaria prediction ، Primary health centers (PHCs) ، Big data ، Artificial neural networks (ANNs) |
مقاله انگلیسی |
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ارزیابی از متابولیسم بالینی دارویی برهمکنش دارو از داروی ضد مالاریای α/β- ارتریر و سولفادوکسین-پریمتامین
سال انتشار: 2017 - تعداد صفحات فایل pdf انگلیسی: 26 - تعداد صفحات فایل doc فارسی: 25 ترکیب های درمانی داروی ضد مالاریا اکنون استفاده گسترده ای برای درمانی از مالاریای بدون عارضه است. هدف از مطالعه اخیر بررسی اثری از تزریق همزمان بین مولکولی ارتریر α/β (AE) و سولفادوکسین –پریمتامین خوراکی (SP) بر روی خواص فارموکوکنتیک از هر دارو به عنوان یک مطالعه برهمکنش دارو-دارو برای حمایت از توسعه درمان با دوز ثابت ترکیب ها است. یک ازمایش بالینی تک دوزی، باز، و متقاطع در مرد داوطلب هندی بزرگسال سالم (18-45 سال، n=13) اجرا شده است، دریافت تک دوز از AE, SP و دوزی ترکیبی از AE و SP انجام شد. نمونه خون به مدت 21 روز پس از تزریق جمع اوری شد و غلظتی از الفا- ارتریر، بتا- ارتریر، سولفادوکسین و پریمتامین به وسیله استفاده از روش اسپکتروسکوپی جرمی کروماتوگرافی مایع تایید شده تعیین شد. پارامترهای فارماکوکنتیک تعیین شده و انالیز اماری با محاسبه نسبت هندسی مهم و فاصله های مطمئن تجزیه و تحلیل شد. پس از یک بار مصرف دوز تزریق عضلانی از AE بین مولکولی و SP خوراکیف خواص فارماکوکنتیک از الفا-بتا ارتریر به طور قابل ملاحظه ای بر روی خواص فارماکوکنتیک از SP در گروه انتخابی از داوطلبان سالم تحت اثر قرار نگرفت. با وجود این، تحقیقات بیشتر در اینده دارای اهمیت است.
کلمات کلیدی: فارماکوکینتیک | مالاریا | درمان ترکیبی | α / β-arteether | الفادوکسیین | پیریمتیامین |
مقاله ترجمه شده |
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Malavefes: A computational voice-enabled malaria fuzzy informatics software for correct dosage prescription of anti-malarial drugs
مالاوفس: یک نرم افزار انفورماتیکی محاسباتی صوتی فازی مالاریا برای تجویز صحیح دز داروهای ضد مالاریا-2017 Malaria is one of the infectious diseases consistently inherent in many Sub-Sahara African countries.
Among the issues of concern are the consequences of wrong diagnosis and dosage administration of
anti-malarial drugs on sick patients; these have resulted into various degrees of complications ranging
from severe headaches, stomach and body discomfort, blurred vision, dizziness, hallucinations, and in
extreme cases, death. Many expert systems have been developed to support different infectious disease
diagnoses, but not sure of any yet, that have been specifically designed as a voice-based application to
diagnose and translate malaria patients’ symptomatic data for pre-laboratory screening and correct pre
scription of proper dosage of the appropriate medication. We developed Malavefes, (a malaria voice
enabled computational fuzzy expert system for correct dosage prescription of anti-malarial drugs) using
Visual Basic.NET., and Java programming languages. Data collation for this research was conducted by
survey from existing literature and interview from public health experts. The database for this malaria
drug informatics system was implemented using Microsoft Access. The Root Sum Square (RSS) was
implemented as the inference engine of Malavefes to make inferences from rules, while Centre of
Gravity (CoG) was implemented as the defuzzification engine. The drug recommendation module was
voice-enabled. Additional anti-malaria drug expiration validation software was developed using Java pro
gramming language. We conducted a user-evaluation of the performance and user-experience of the
Malavefes software.
Keywords: Informatics | Bioinformatics | Fuzzy | Anti-malaria | Voice computing | Dosage prescription |
مقاله انگلیسی |
6 |
Novel image processing approach to detect malaria
یک رویکرد پردازش تصویر برای تشخیص مالاریا-2015 In this paper we present a novel image processing algorithm providing good preliminary capabilities for
in vitro detection of malaria. The proposed concept is based upon analysis of the temporal variation of
each pixel. Changes in dark pixels mean that inter cellular activity happened, indicating the presence of
the malaria parasite inside the cell. Preliminary experimental results involving analysis of red blood cells
being either healthy or infected with malaria parasites, validated the potential benefit of the proposed
numerical approach.
Keywords:
Malaria detection
Image processing
Microscopy
Biomedicine |
مقاله انگلیسی |
7 |
روش جدید پردازش تصویر برای تشخیص مالاریا
سال انتشار: 2015 - تعداد صفحات فایل pdf انگلیسی: 6 - تعداد صفحات فایل doc فارسی: 16 در این مقاله ما الگوریتم جدیدی را برای پردازش تصویر معرفی می کنیم که قابلیت های اولیه ای را برای تشخیص برون تنی مالاریا فراهم می کند. مفهوم ارائه شده بر اساس تحلیل تغییرات موقت هر پیکسل است. تغییر در پیکسل های تاریک بدان معناست که فعالیت های درون سلولی رخ داده است و این امر نشان دهنده ی وجود انگل مالاریا در درون سلول است. نتایج اولیه ی آزمایشات شامل تحلیل سلول های قرمز خونی سالم یا عفونی فایده ی بالقوه ی روش شمارشی ارائه شده را معتبر می سازد.
واژه های کلیدی: تشخیص مالاریا | پردازش تصویر | میکروسکوپ | بیومدیکال
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مقاله ترجمه شده |