Automatic Knee Injury Identification through Thermal Image Processing and Convolutional Neural Networks

Knee injury is a common health problem that affects both people who practice sports and those who do not do it. The high prevalence of knee injuries produces a considerable impact on the health-related life quality of patients. For this reason, it is essential to develop procedures for an early diag...

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Bibliographic Details
Published in:Electronics (Basel) 2022-12, Vol.11 (23), p.3987
Main Authors: Trejo-Chavez, Omar, Amezquita-Sanchez, Juan P., Huerta-Rosales, Jose R., Morales-Hernandez, Luis A., Cruz-Albarran, Irving A., Valtierra-Rodriguez, Martin
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Arthritis
Artificial neural networks
Deep learning
Diagnostic imaging
Image processing
Infrared imagery
Infrared imaging
Injuries
Injury prevention
Knee
Machine learning
Magnetic resonance imaging
Medical imaging
Methods
Neural networks
Object recognition (Computers)
Pattern recognition
Robustness
Skin
Thermal imaging
Thermography
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Summary:Knee injury is a common health problem that affects both people who practice sports and those who do not do it. The high prevalence of knee injuries produces a considerable impact on the health-related life quality of patients. For this reason, it is essential to develop procedures for an early diagnosis, allowing patients to receive timely treatment for preventing and correcting knee injuries. In this regard, this paper presents, as main contribution, a methodology based on infrared thermography (IT) and convolutional neural networks (CNNs) to automatically differentiate between a healthy knee and an injured knee, being an alternative tool to help medical specialists. In general, the methodology consists of three steps: (1) database generation, (2) image processing, and (3) design and validation of a CNN for automatically identifying a patient with an injured knee. In the image-processing stage, grayscale images, equalized images, and thermal images are obtained as inputs for the CNN, where 98.72% of accuracy is obtained by the proposed method. To test its robustness, different infrared images with changes in rotation angle and different brightness levels (i.e., possible conditions at the time of imaging) are used, obtaining 97.44% accuracy. These results demonstrate the effectiveness and robustness of the proposal for differentiating between a patient with a healthy knee and an injured knee, having the advantages of using a fast, low-cost, innocuous, and non-invasive technology.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics11233987