publication . Article . Preprint . 2020

Assessing Automated Machine Learning Service to Detect COVID-19 from X-Ray and CT Images: A Real-Time Smartphone Application Case Study

Mohammad Razib Mustafiz; Khaled Mohsin;
Open Access
  • Published: 02 Oct 2020
Comment: 21 Pages, 6 Tables
ACM Computing Classification System: ComputingMethodologies_PATTERNRECOGNITION
free text keywords: COVID-19; Machine Learning; Custom Vision; Auto ML; Smartphone Application; GAN; Deep Learning; Transfer Learning; X-Ray; CT; CNN, COVID-19; Machine Learning; Custom Vision; Auto ML; Smartphone Application; GAN; Deep Learning; Transfer Learning; X-Ray; CT; CNN, Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, 03, F.2.2, algebra_number_theory, System on a chip, Computer science, Smartphone application, Mobile device, Latency (engineering), Artificial neural network, Machine learning, computer.software_genre, computer, Coronavirus disease 2019 (COVID-19), Artificial intelligence, business.industry, business, Inference, Internet access, business.product_category, Computer vision

16. Microsoft_Corp. Microsoft Custom Vision. Secondary Microsoft Custom Vision. Accessed May2020. Alphabet_Inc. Cloud AutoML. Secondary Cloud AutoML. Accessed May 2020. Apple_Inc. Create ML. Secondary Create ML. Accessed May 2020. Microsoft_Corp. Microsoft Custom Vision. . Accessed May 2020 Joseph Paul Cohen, Paul Morrison, Lan Dao et al. COVID-19 image data collection, arXiv:2003.11597, 2020 Accessed May 2020 Chest X-Ray images (Pneumonia) by Paul Timothy Mooney, Developer Advocate at Accessed May 2020 Italian society of Medical and Interventional Radiology. Accessed May 2020 Caltech Home Objects Dataset. Accessed May 2020 Use your model with the prediction API. Accessed May 2020 Sample iOS application for CoreML models exported from Custom Vision Service. . Accessed 2020 Thomas Cherian, E Kim Mulholland, John B Carlin et al. Standardized interpretation of paediatric chest radiographs for the diagnosis of pneumonia in epidemiological studies. Bulletin of the World Health Organization, 83:353-359, 2005.

T Franquet. Imaging of pneumonia: trends and algorithms. European Respiratory Journal, 18(1):196-208, 2001. [OpenAIRE]

C. Dallet, S. Kareem and I. Kale, "Real time blood image processing application for malaria diagnosis using mobile phones," 2014 IEEE International Symposium on Circuits and Systems (ISCAS), Melbourne VIC, 2014, pp. 2405-2408, doi: 10.1109/ISCAS.2014.6865657.

D. Jayatilake, K.Suzuki,Y.Teramoto et al., "Swallowscope: A smartphone based device for the assessment of swallowing ability," IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Valencia, 2014, pp. 697-700, doi: 10.1109/BHI.2014.6864459.

J. J. Oresko, Z.Jin,J.Cheng et al. "A Wearable Smartphone-Based Platform for Real-Time Cardiovascular Disease Detection Via Electrocardiogram Processing," in IEEE Transactions on Information Technology in Biomedicine, vol. 14, no. 3, pp. 734-740, May 2010, doi: 10.1109/TITB.2010.2047865.

Michael G. Mauk, Calling in the test: Smartphone-based urinary sepsis diagnostics,

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