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Deep Convolutional Neural Networks for Microscopy-Based Point of Care Diagnostics [article]

John A. Quinn, Rose Nakasi, Pius K. B. Mugagga, Patrick Byanyima, William Lubega, Alfred Andama
2016 arXiv   pre-print
In this paper, we evaluate the performance of deep convolutional neural networks on three different microscopy tasks: diagnosis of malaria in thick blood smears, tuberculosis in sputum samples, and intestinal  ...  Point of care diagnostics using microscopy and computer vision methods have been applied to a number of practical problems, and are particularly relevant to low-income, high disease burden areas.  ...  Specification and training of convolutional neural networks Convolutional neural networks (CNNs) are a form of neural network particularly well adapted to the processing of images.  ... 
arXiv:1608.02989v1 fatcat:2k3azhaqufgblkhjgqwuaawrtq

Low-Power Hardware-Based Deep-Learning Diagnostics Support Case Study [article]

Khushal Sethi, Vivek Parmar, Manan Suri
2022 arXiv   pre-print
As significant proportion of deep learning research focuses on vision based applications, there exists a potential for using some of these techniques to enable low-power portable health-care diagnostic  ...  In this paper, we propose an embedded-hardware-based implementation of microscopy diagnostic support system for PoC case study on: (a) Malaria in thick blood smears, (b) Tuberculosis in sputum samples,  ...  CONCLUSION In this paper, we demonstrate a low-power portable dedicated hardware-based solution for microscopy point of care diagnostic support.  ... 
arXiv:2209.01507v1 fatcat:oytf3sjdfvhprkv63pbmde5ycm

Human Blastocyst Classification after In Vitro Fertilization Using Deep Learning [article]

Ali Akbar Septiandri, Ade Jamal, Pritta Ameilia Iffanolida, Oki Riayati, Budi Wiweko
2020 arXiv   pre-print
The model presented could be developed into an automated embryo assessment method in point-of-care settings.  ...  Variability among assessors, however, remains one of the main causes of the low success rate of IVF. This study aims to develop an automated embryo assessment based on a deep learning model.  ...  From MRI for brain imaging [6] , various anatomical areas [7] , to point-of-care diagnostics from microscopy images [8] , deep learning has aided medical practitioners to diagnose better.  ... 
arXiv:2008.12480v1 fatcat:f6srykq3rra5toe7tvsxyypmi4

Hardware-Efficient Stochastic Binary CNN Architectures for Near-Sensor Computing

Vivek Parmar, Bogdan Penkovsky, Damien Querlioz, Manan Suri
2022 Frontiers in Neuroscience  
To further demonstrate its application for real-world scenarios, we present a case-study of microscopy image diagnostics for pathogen detection.  ...  With recent advances in the field of artificial intelligence (AI) such as binarized neural networks (BNNs), a wide variety of vision applications with energy-optimized implementations have become possible  ...  “Deep convolutional neural networks for microscopy-based point of care diagnostics,” in Machine Learning for Healthcare Conference (Los Angeles, CA), 271–281.  ... 
doi:10.3389/fnins.2021.781786 pmid:35069101 pmcid:PMC8766965 fatcat:7xpaoytyg5ftpa4ng3jau3dkyi

Assessing kidney stone composition using smartphone microscopy and deep neural networks

Ege Gungor Onal, Hakan Tekgul
2022 BJUI Compass  
To propose a point-of-care image recognition system for kidney stone composition classification using smartphone microscopy and deep convolutional neural networks.  ...  We demonstrate a rapid and accurate point of care diagnostics method for classifying the four types of kidney stones.  ...  ACKNOWLEDGEMENTS This project was supported in part by the Scientific and Technological Research Council of Turkey (TUBITAK).  ... 
doi:10.1002/bco2.137 pmid:35783589 pmcid:PMC9231678 fatcat:rsd3qptesvdulo77txwqtnqp7a

Classification of Malaria-Infected Cells Using Deep Convolutional Neural Networks [chapter]

W. David Pan, Yuhang Dong, Dongsheng Wu
2018 Machine Learning - Advanced Techniques and Emerging Applications  
, in light of the overfitting problem associated with training deep convolutional neural networks.  ...  We present some of our recent progresses on highly accurate classification of malaria-infected cells using deep convolutional neural networks.  ...  In [16] were described point-of-care diagnostics using microscopes and smartphones, where deep convolutional neural network (CNN) was employed to identify image patches suspected to contain malaria-infected  ... 
doi:10.5772/intechopen.72426 fatcat:adrobinr7jd4pfq5ifrmpsmhf4

MOSQUITO-NET: A deep learning based CADx system for malaria diagnosis along with model interpretation using GradCam and class activation maps [article]

Aayush Kumar, Sanat B Singh, Suresh Chandra Satapathy, Minakhi Rout
2020 arXiv   pre-print
State of the art Computer-aided diagnostic techniques based on deep learning algorithms such as CNNs, with end to end feature extraction and classification, have widely contributed to various image recognition  ...  Due to the unavailability of resources, its diagnostic accuracy is largely affected by large scale screening.  ...  Conclusion In this study, we introduced Mosquito-Net, a deep convolutional neural network for the detection of malaria cases from blood smear images.  ... 
arXiv:2006.10547v2 fatcat:j2ncpuckdjbhlb5p6hegqtvg3u

Toward a Thinking Microscope

Yair Rivenson, Aydogan Ozcan
2018 Optics and photonics news (Print)  
We discuss recently emerging applications of the state-of-art deep learning methods on optical microscopy and microscopic image reconstruction, which enable new transformations among different modes and  ...  We believe that deep learning will fundamentally change both the hardware and image reconstruction methods used in optical microscopy in a holistic manner.  ...  A trained convolutional neural network enhances the images acquired by a smartphone-based microscope.  ... 
doi:10.1364/opn.29.7.000034 fatcat:ekubq7auyzcalh354cvj6o7fny

Computational Optics Enables Breast Cancer Profiling in Point-of-Care Settings

Jouha Min, Hyungsoon Im, Matthew Allen, Phillip J. McFarland, Ismail Degani, Hojeong Yu, Erica Normandin, Divya Pathania, Jaymin M. Patel, Cesar M. Castro, Ralph Weissleder, Hakho Lee
2018 ACS Nano  
The global burden of cancer, severe diagnostic bottlenecks in underserved regions, and underfunded health care systems are fueling the need for inexpensive, rapid, and treatment-informative diagnostics  ...  Here, we show high accuracy (>90%) in (i) recognizing cells directly from diffraction patterns and (ii) classifying breast cancer types using deep-learning-based analysis of sample aspirates.  ...  point-of-care operation of the technology.  ... 
doi:10.1021/acsnano.8b03029 pmid:30113824 pmcid:PMC6519708 fatcat:mq2thcp2nbdjvmec6zjhukrnze

Automated Blood Cell Detection and Counting via Deep Learning for Microfluidic Point-of-Care Medical Devices [article]

Tiancheng Xia, Richard Jiang, YongQing Fu, Nanlin Jin
2019 arXiv   pre-print
The approach we used in this study was based on Faster Region-based Convolutional Neural Networks (Faster RCNNs), and a transfer learning process was applied to apply this technique to the microscopic  ...  methods, implying a promising future of this technology to be applied to the microfluidic point-of-care medical devices.  ...  Point-of-care testing (or bedside testing) is generally defined as medical diagnostic testing at or near the point of caor in other words, at the time and place of patient care.  ... 
arXiv:1909.05393v1 fatcat:y6u2yw7yhvh2vio3pulv5zcxry

Identify the stiffness of DNA via deep learning [article]

Haiqian Yang, Liu Yang, Shaobao Liu
2019 arXiv   pre-print
DNA detection is of great significance in the point-of-care diagnostics.  ...  We identified the stiffness of DNA with the trained convolutional neural network on the simulated image set. The identification accuracy reached 99.85%.  ...  XCA1816310), and by the Foundation for the Priority Academic Program Development of Jiangsu Higher Education Institutions.  ... 
arXiv:1908.01268v1 fatcat:qaqjycv5szddplh42ldjv7oosu

Deep Learning in Image Cytometry: A Review

Anindya Gupta, Philip J. Harrison, Håkan Wieslander, Nicolas Pielawski, Kimmo Kartasalo, Gabriele Partel, Leslie Solorzano, Amit Suveer, Anna H. Klemm, Ola Spjuth, Ida‐Maria Sintorn, Carolina Wählby
2018 Cytometry Part A  
Artificial intelligence, deep convolutional neural networks, and deep learning are all niche terms that are increasingly appearing in scientific presentations as well as in the general media.  ...  Starting with an analogy to neuroscience, we aim to give the reader an overview of the key concepts of neural networks, and an understanding of how deep learning differs from more classical approaches  ...  Ewert Bengtsson, and Petter Ranefall for their appreciative suggestions. LITERATURE CITED  ... 
doi:10.1002/cyto.a.23701 pmid:30565841 pmcid:PMC6590257 fatcat:dszbcsfncrhxnazsxopjkbe3ju

Biosensors and Machine Learning for Enhanced Detection, Stratification, and Classification of Cells: A Review [article]

Hassan Raji, Muhammad Tayyab, Jianye Sui, Seyed Reza Mahmoodi, Mehdi Javanmard
2021 arXiv   pre-print
Sensors focusing on the detection and stratification of cells have gained popularity as technological advancements have allowed for the miniaturization of various components inching us closer to Point-of-Care  ...  Understanding how they function and differentiating cells from one another therefore is of paramount importance for disease diagnostics as well as therapeutics.  ...  Convolutional Neural Networks A specific form of ANNs is called Convolutional Neural Nets (CNNs).  ... 
arXiv:2101.01866v1 fatcat:rws7k3yp6ndmnlkqcvafmkgphi

Dermatological Disorder Classification

Atharva Kulkarni, Chirag Mahajan, Tejas Hasabnis
2022 International Journal for Research in Applied Science and Engineering Technology  
A lot of annotated pictures are needed for the training of neural network-based diagnosis algorithms, however the quantity of high-quality dermatoscopic images with accurate diagnoses is small or restricted  ...  Recent improvements in graphics card power and machine learning methods have boosted hopes for the availability of automated diagnostic systems that can quickly identify all types of pigmented skin lesions  ...  Additionally, we would like to express our gratitude to all the designers of the websites, programmes, and other features that served as inspiration or references for the development of this system.  ... 
doi:10.22214/ijraset.2022.46516 fatcat:gbwvmbyajbgunduzqoqvepc6a4

Deep learning-based holographic polarization microscopy [article]

Tairan Liu, Kevin de Haan, Bijie Bai, Yair Rivenson, Yi Luo, Hongda Wang, David Karalli, Hongxiang Fu, Yibo Zhang, John FitzGerald, Aydogan Ozcan
2020 arXiv   pre-print
Using a deep neural network, the reconstructed holographic images from a single state of polarization can be transformed into images equivalent to those captured using a single-shot computational polarized  ...  Here, we present a deep learning-based holographic polarization microscope that is capable of obtaining quantitative birefringence retardance and orientation information of specimen from a phase recovered  ...  The authors acknowledge the support of National Institutes of Health (NIH, R21AR072946).  ... 
arXiv:2007.00741v1 fatcat:5ik5gcu3ezb6nd77r3t2beqeqm
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