Artificial Intelligence-Based Infrared Thermal Image Processing and Its Applications

Thermal imaging is used to diagnose medical conditions, and a computer diagnostics system (CAD) based on thermographic data is developed. Customized deep learning models are used for accurate diagnosis, and new aspects in rheumatoid arthritis and diabetes research are explored with advanced analytical tools. Feature fusion and reduction algorithms are used for accurate classification of images.

Format: Hardback
Length: 230 pages
Publication date: 28 September 2022
Publisher: Taylor & Francis Ltd

Thermal imaging has emerged as a powerful tool in the field of medicine, offering a non-invasive means of diagnosing a wide range of medical conditions. Image processing techniques play a crucial role in enhancing the diagnostic capabilities of thermal images. In this article, we will explore the applications of various image processing techniques in thermal imaging applications for the diagnosis of different medical conditions.

Thermal imaging provides a unique perspective on the body's thermal properties, which can be used to detect abnormalities and diagnose various medical conditions. Image processing techniques such as segmentation, filtering, and enhancement can be applied to thermal images to improve their clarity and visibility. For example, segmentation can be used to separate the different tissue types in a thermal image, making it easier for healthcare professionals to identify areas of interest. Filtering can be used to remove noise and artifacts from the image, improving the accuracy of the diagnosis. Enhancement techniques can be used to highlight subtle changes in temperature that may be indicative of underlying health conditions.

One of the most promising applications of thermal imaging in medicine is in the field of computer diagnostics. Computer-aided diagnostics (CAD) systems use thermographic data to diagnose a wide range of medical conditions. CAD systems can analyze thermal images to identify patterns and abnormalities that may be indicative of underlying health conditions. For example, CAD systems can detect changes in skin temperature that may be associated with rheumatoid arthritis or diabetes.

Customized deep learning models have also been developed for accurate diagnosis of various diseases. Deep learning algorithms are capable of analyzing large amounts of data and identifying patterns that may be difficult for humans to detect. Customized deep learning models can be trained on thermal images of patients with specific medical conditions to generate accurate diagnoses. For example, customized deep learning models can be used to diagnose skin cancer or breast cancer.

In addition to diagnosis, thermal imaging has also been used in research to explore new aspects of rheumatoid arthritis and diabetes. Advanced analytical tools such as machine learning and artificial intelligence have been used to analyze thermal images and extract valuable information. For example, machine learning algorithms have been used to identify patterns in thermal images that may be associated with the progression of rheumatoid arthritis. Artificial intelligence algorithms have been used to analyze thermal images and predict the risk of developing diabetes.

Feature fusion algorithms and feature reduction algorithms have also been used for accurate classification of images. Feature fusion algorithms combine multiple features from different sources to generate a more accurate representation of the image. Feature reduction algorithms reduce the number of features in the image, making it easier for the algorithm to identify patterns and abnormalities. For example, feature fusion algorithms can be used to combine thermal images with other medical imaging modalities such as X-ray or MRI to generate a more comprehensive diagnosis.

In conclusion, thermal imaging has emerged as a powerful tool in the field of medicine, offering a non-invasive means of diagnosing a wide range of medical conditions. Image processing techniques play a crucial role in enhancing the diagnostic capabilities of thermal images. Customized deep learning models, feature fusion algorithms, and feature reduction algorithms have been developed to improve the accuracy of diagnosis and explore new aspects of rheumatoid arthritis and diabetes research. As thermal imaging technology continues to evolve, it is likely to play an increasingly important role in healthcare.

Weight: 620g
Dimension: 229 x 152 (mm)
ISBN-13: 9781032158143