Smartphone‐based detection of <scp>COVID</scp>‐19 and associated pneumonia using thermal imaging and a transfer learning algorithm

Hoffer, Oshrit; Brzezinski, Rafael Y.; Ganim, Adam; Shalom, Perry; Ovadia‐Blechman, Zehava; Ben‐Baruch, Lital; Lewis, Nir; Peled, Racheli; Shimon, Carmi; Naftali‐Shani, Nili; Katz, Eyal; Zimmer, Yair; Rabin, Neta

doi:10.1002/jbio.202300486

Cited by 4 publications

(1 citation statement)

References 31 publications

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“…Their best approach provided a classification accuracy of 94%. A CNN-based transfer learning algorithm called ResNet50 was used to classify thermal images of pneumonia from COVID-19 from 101 patients, providing an accuracy of 91% [44]. Their training and test images were randomly selected from a pool of data which consisted of several images from the same patient.…”

Section: Machine Learning and Deep Learning Techniquesmentioning

confidence: 99%

Convolutional Neural Network to Classify Infrared Thermal Images of Fractured Wrists in Pediatrics

Shobayo,

Saatchi,

Ramlakhan

2024

Healthcare

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Convolutional neural network (CNN) models were devised and evaluated to classify infrared thermal (IRT) images of pediatric wrist fractures. The images were recorded from 19 participants with a wrist fracture and 21 without a fracture (sprain). The injury diagnosis was by X-ray radiography. For each participant, 299 IRT images of their wrists were recorded. These generated 11,960 images (40 participants × 299 images). For each image, the wrist region of interest (ROI) was selected and fast Fourier transformed (FFT) to obtain a magnitude frequency spectrum. The spectrum was resized to 100 × 100 pixels from its center as this region represented the main frequency components. Image augmentations of rotation, translation and shearing were applied to the 11,960 magnitude frequency spectra to assist with the CNN generalization during training. The CNN had 34 layers associated with convolution, batch normalization, rectified linear unit, maximum pooling and SoftMax and classification. The ratio of images for the training and test was 70:30, respectively. The effects of augmentation and dropout on CNN performance were explored. Wrist fracture identification sensitivity and accuracy of 88% and 76%, respectively, were achieved. The CNN model was able to identify wrist fractures; however, a larger sample size would improve accuracy.

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Section: Machine Learning and Deep Learning Techniquesmentioning

confidence: 99%

Convolutional Neural Network to Classify Infrared Thermal Images of Fractured Wrists in Pediatrics

Shobayo,

Saatchi,

Ramlakhan

2024

Healthcare

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Novel Diagnostic Approach for Acute Pharyngitis: Combining Machine Learning With Thermal Imaging

Hoffer,

Cohen,

Gerstein

et al. 2024

Journal of Biophotonics

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We evaluated the effect of infrared thermography (IRT) on the clinical assessment of bacterial and viral pharyngitis and its impact on the predictive value of the McIsaac score algorithm for streptococcal pharyngitis in children. We also investigated if IRT could distinguish between bacterial and viral pharyngitis. The study included children aged 2–17 years presenting with sore throat and fever over 38°C from November 1, 2021, to April 30, 2022. Of the 76 assessed children, 16 were excluded due to missing data or technical issues, leaving 60 children (32 males, 28 females) divided into three groups: Group A with streptococcal pharyngitis (N = 30), viral pharyngitis (N = 16), and healthy controls (N = 14). McIsaac score and IRT imaging showed a 90% positive predictive value for streptococcal pharyngitis. While IRT alone could not distinguish between bacterial and viral infections, it significantly increased the predictive value when combined with the McIsaac score.

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2nd Spring Biophotonics Conference in Porto

Oliveira,

Meglinski,

Gannot

et al. 2024

Journal of Biophotonics

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Smartphone‐based detection of COVID‐19 and associated pneumonia using thermal imaging and a transfer learning algorithm

Cited by 4 publications

References 31 publications

Convolutional Neural Network to Classify Infrared Thermal Images of Fractured Wrists in Pediatrics

Convolutional Neural Network to Classify Infrared Thermal Images of Fractured Wrists in Pediatrics

Novel Diagnostic Approach for Acute Pharyngitis: Combining Machine Learning With Thermal Imaging

2nd Spring Biophotonics Conference in Porto

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