Deep Learning in Cancer and Infectious Disease

The value of pulmonary function test (PFT) data is increasing due to the advent of the Coronavirus Infectious Disease 19 (COVID-19) and increased respiratory disease. However, these PFT data cannot be directly used in clinical studies, because PFT results are stored in raw image files. In this study, the classification and itemization medical image (CIMI) system generates valuable data from raw PFT images by automatically classifying various PFT results, extracting texts, and storing them in the PFT database and Excel files. The deep-learning-based optical character recognition (OCR) technology was mainly used in CIMI to classify and itemize PFT images in St. Mary’s Hospital. CIMI classified seven types and itemized 913,059 texts from 14,720 PFT image sheets, which cannot be done by humans. The number, type, and location of texts that can be extracted by PFT type are all different, but CIMI solves this issue by classifying the PFT image sheets by type, allowing researchers to analyze the data. To demonstrate the superiority of CIMI, the validation results of CIMI were compared to the results of the other four algorithms. A total of 70 randomly selected sheets (ten sheets from each type) and 33,550 texts were used for the validation. The accuracy of CIMI was 95%, which was the highest accuracy among the other four algorithms.

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Predicting Infectious Disease Using Deep Learning and Big Data

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph15081596 ◽

2018 ◽

Vol 15 (8) ◽

pp. 1596 ◽

Cited By ~ 59

Author(s):

Sangwon Chae ◽

Sungjun Kwon ◽

Donghyun Lee

Keyword(s):

Infectious Disease ◽

Big Data ◽

Deep Learning ◽

Infectious Diseases ◽

Short Term Memory ◽

Moving Average ◽

Surveillance Systems ◽

Social Media Data ◽

Autoregressive Integrated Moving Average ◽

Media Data

Infectious disease occurs when a person is infected by a pathogen from another person or an animal. It is a problem that causes harm at both individual and macro scales. The Korea Center for Disease Control (KCDC) operates a surveillance system to minimize infectious disease contagions. However, in this system, it is difficult to immediately act against infectious disease because of missing and delayed reports. Moreover, infectious disease trends are not known, which means prediction is not easy. This study predicts infectious diseases by optimizing the parameters of deep learning algorithms while considering big data including social media data. The performance of the deep neural network (DNN) and long-short term memory (LSTM) learning models were compared with the autoregressive integrated moving average (ARIMA) when predicting three infectious diseases one week into the future. The results show that the DNN and LSTM models perform better than ARIMA. When predicting chickenpox, the top-10 DNN and LSTM models improved average performance by 24% and 19%, respectively. The DNN model performed stably and the LSTM model was more accurate when infectious disease was spreading. We believe that this study’s models can help eliminate reporting delays in existing surveillance systems and, therefore, minimize costs to society.

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Ensemble deep learning for tuberculosis detection

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v17.i2.pp1014-1020 ◽

2020 ◽

Vol 17 (2) ◽

pp. 1014

Author(s):

Mohd Hanafi Ahmad Hijazi ◽

Leong Qi Yang ◽

Rayner Alfred ◽

Hairulnizam Mahdin ◽

Razali Yaakob

Keyword(s):

Infectious Disease ◽

Machine Learning ◽

Lung Cancer ◽

Mycobacterium Tuberculosis ◽

Deep Learning ◽

Tubercle Bacillus ◽

X Ray ◽

Initial Stage ◽

The World ◽

Chest X Ray

Tuberculosis (TB) is one of the deadliest infectious disease in the world. TB is caused by a type of tubercle bacillus called Mycobacterium Tuberculosis. Early detection of TB is pivotal to decrease the morbidity and mortality. TB is diagnosed by using the chest x-ray and a sputum test. Challenges for radiologists are to avoid confused and misdiagnose TB and lung cancer because they mimic each other. Semi-automated TB detection using machine learning found in the literature requires identification of objects of interest. The similarity of tissues, veins and small nodules presenting the image at the initial stage may hamper the detection. In this paper, an approach to detect TB, that does not require segmentation of objects of interest, based on ensemble deep learning, is presented. Evaluation on publicly available datasets show that the proposed approach produced a model that recorded the best accuracy, sensitivity and specificity of 91.0%, 89.6% and 90.7% respectively.

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Associating Unemployment with Panic Attack Using Deep Learning during COVID-19

Journal of Engineering Research and Reports ◽

10.9734/jerr/2020/v17i317190 ◽

2020 ◽

pp. 33-40

Author(s):

Shawni Dutta ◽

Samir Kumar Bandyopadhyay

Keyword(s):

Infectious Disease ◽

Deep Learning ◽

Panic Attack ◽

Panic Attacks ◽

Single Model ◽

Panic Disorders ◽

Learning Framework ◽

Learning Techniques ◽

Proposed Model ◽

Mlp Classifier

Corona Virus Infectious Disease (COVID-19) is newly emerging infectious disease. It is known to the world in late 2019. Due to this, the mental health of employees is disturbed. There is always a fear of unemployment amongst employees due to the present scenario of lockdown. This may even create a panic attack. It has been happening rapidly during COVID-19. It has a great effect on human health. This paper analyses multiple factors that have an impact on causing panic attacks. Deep Learning techniques are explored which detects panic disorders on people. Recurrent Neural Network (RNN) based deep learning framework is utilized in this paper that assembles multiple RNN layers along with other hyper-parameters into a single model. This method is implemented by capturing interfering factors and predicts the panic attack tendency of people during COVID-19. Early prediction of panic attacks may assist in saving life from unwanted circumstances. It is also observed that comparative study between MLP and stacked-RNN classifier indicates significantly better results of proposed model over MLP classifier in terms of evaluating metrics.

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Advanced deep learning algorithms for infectious disease modeling using clinical data- A Case Study on CoVID-19

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405617666210908125911 ◽

2021 ◽

Vol 17 ◽

Author(s):

Ajay Kumar ◽

Smita Nivrutti Kolnure ◽

Kumar Abhishek ◽

Fadi-Al-Turjman ◽

Pranav Nerurkar ◽

...

Keyword(s):

Infectious Disease ◽

Deep Learning ◽

Time Series Data ◽

Disease Modeling ◽

Learning Algorithms ◽

Image Data ◽

Series Data ◽

Text Data ◽

Infectious Disease Modeling

Background: Infectious disease happens when an individual is defiled by a micro-organism/virus from another person or an animal. It is troublesome that causes hurt at both individual and huge scope scales. Case Presentation : The ongoing episode of COVID-19 ailment brought about by the new coronavirus first distinguished in Wuhan China, and its quick spread far and wide, revived the consideration of the world towards the impacts of such plagues on individual’s regular daily existence. We attempt to exploit this effectiveness of Advanced deep learning algorithms to predict the Growth of Infectious disease based on time series data and classification based on (symptoms) text data and X-ray image data. Conclusion: Goal is identifying the nature of the phenomenon represented by the sequence of observations and forecasting.

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Tissue section lifting for electron microscopy

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100081292 ◽

1978 ◽

Vol 36 (2) ◽

pp. 86-87

Author(s):

Adrian F. van Dellen

Keyword(s):

Infectious Disease ◽

Electron Microscopy ◽

Tissue Culture ◽

Organ System ◽

Surrounding Tissue ◽

Paraffin Sections ◽

Surface Areas ◽

Specific Determination ◽

High Degree ◽

Accuracy And Repeatability

The morphologic pathologist may require information on the ultrastructure of a non-specific lesion seen under the light microscope before he can make a specific determination. Such lesions, when caused by infectious disease agents, may be sparsely distributed in any organ system. Tissue culture systems, too, may only have widely dispersed foci suitable for ultrastructural study. In these situations, when only a few, small foci in large tissue areas are useful for electron microscopy, it is advantageous to employ a methodology which rapidly selects a single tissue focus that is expected to yield beneficial ultrastructural data from amongst the surrounding tissue. This is in essence what "LIFTING" accomplishes. We have developed LIFTING to a high degree of accuracy and repeatability utilizing the Microlift (Fig 1), and have successfully applied it to tissue culture monolayers, histologic paraffin sections, and tissue blocks with large surface areas that had been initially fixed for either light or electron microscopy.

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