Empirical Review of Various Thermography-based Computer-aided Diagnostic Systems for Multiple Diseases

ABSTRACT

The lifestyle led by today's generation and its negligence towards health is highly susceptible to various diseases. Developing countries are at a higher risk of mortality due to late-stage presentation, inaccessible diagnosis, and high-cost treatment. Thermography-based technology, aided with machine learning, for screening inflammation in the human body is non-invasive and cost-wise appropriate. It requires very little equipment, especially in rural areas with limited facilities. Recently, Thermography-based monitoring has been deployed worldwide at various organizations and public gathering points as a first measure of screening COVID-19 patients. In this article, we systematically compare the state-of-the-art feature extraction approaches for analyzing thermal patterns in the human body, individually and in combination, on a platform using three publicly available Datasets of medical thermal imaging, four Feature Selection methods, and four well-known Classifiers, and analyze the results. We developed and used a two-level sampling method for training and testing the classification model. Among all the combinations considered, the classification model with Unified Feature-Sets gave the best performance for all the datasets. Also, the experimental results show that the classification accuracy improves considerably with the use of feature selection methods. We obtained the best performance with a features subset of 45, 57, and 39 features (from Unified Feature Set) with a combination of mRMR and SVM for DB-DMR-IR and DB-FOOT-IR and a combination of ReF and RF for DB-THY-IR. Also, we found that for all the feature subsets, the features obtained are relevant, non-redundant, and distinguish normal and abnormal thermal patterns with the accuracy of 94.75% on the DB-DMR-IR dataset, 93.14% on the DB-FOOT-IR dataset, and 92.06% on the DB-THY-IR dataset. [ FROM AUTHOR] Copyright of ACM Transactions on Intelligent Systems & Technology is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)