Infrared Thermal Imaging Analysis in Screening for Toddler’s Fracture: A Proof-of-Concept Study

Saatchi, Reza; Ramlakhan, Shammi

doi:10.3390/app132413299

Cited by 1 publication

(2 citation statements)

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“…This imbalance may bias the analysis and thus affect the accuracy of the results. A study reported a means to deal with this imbalance by using Euclidean distance between samples of the same category to reduce the associated imbalance [31].…”

Section: Convolutional Neural Network and Data Augmentationmentioning

confidence: 99%

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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: Convolutional Neural Network and Data Augmentationmentioning

confidence: 99%

“…A summary of other related studies using IRT imaging for medical diagnosis is provided in Table 1. Repetitive strain injuries 33 Wrist and hand Joints 0.65 [35] Fracture/sprain 133 Various 0.26 to 0.47 [31] Toddler's fracture 39 Tibia 1.1…”

Section: Infrared Thermal Imaging For Medical Diagnosismentioning

confidence: 99%