Infrared imaging segmentation employing an explainable deep neural network

Liao, X., Wang, D., Li, Z., Dey, N., Simon, R.S. ORCID: https://orcid.org/0000-0001-7899-4445 and Shi, F. (2023) Infrared imaging segmentation employing an explainable deep neural network. Turkish Journal of Electrical Engineering & Computer Sciences, 31 (6). pp. 1021-1038. ISSN 1300-0632 doi: 10.55730/1300-0632.4032

Abstract/Summary

Explainable AI (XAI) improved by a deep neural network (DNN) of a residual neural network (ResNet) and long short-term memory networks (LSTMs), termed XAIRL, is proposed for segmenting foot infrared imaging datasets. First, an infrared sensor imaging dataset is acquired by a foot infrared sensor imaging device and preprocessed. The infrared sensor image features are then defined and extracted with XAIRL being applied to segment the dataset. This paper compares and discusses our results with XAIRL. Evaluation indices are applied to perform various measurements for foot infrared image segmentation including accuracy, precision, recall, F1 score, intersection over union (IoU), Dice similarity coefficient, mean intersection of union, boundary displacement error (BDE), Hausdorff distance, and receiver operating characteristic (ROC). Compared to results from the literature, XAIRL shows the highest overall performance, achieving accuracy of 0.93, precision of 0.91, recall of 0.95, and F1 score of 0.93. XAIRL also displays the highest IoU, Dice similarity coefficient, and ROC curve and the lowest BDE and Hausdorff distance. Although U-Net performs well for most metrics, Mask R-CNN shows slightly worse performance but still outperforms the random forest and support vector machine algorithms. By building a high-quality foot infrared imaging dataset, machine learning-based algorithms can accurately analyze foot temperature and pressure distribution. These models can then be used to customize shoes for individual wearers, improving their comfort and reducing the risk of foot injuries, particularly for those with high blood pressure.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/113906
Identification Number/DOI	10.55730/1300-0632.4032
Refereed	Yes
Divisions	Life Sciences > School of Biological Sciences > Biomedical Sciences Life Sciences > School of Biological Sciences > Department of Bio-Engineering
Publisher	TÜBİTAK
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