Automated Analysis Of Diabetic Vasculopathy Using Semantic Segmentation Of Thermal Images Of Peroneal Vessel

Gayatri Joshi and Punal M Arabi

Data and Metadata, 2024, vol. 3, .367

Abstract: Introduction: Diabetic vascular disease is one of most serious health problems in diabetic patients, it causes the development of severe complications including delayed wound healing and increased susceptibility to infections. Methods: To provide accurate and are non-invasive diagnosis, current work emphasizes on Diabetic Vasculopathy (DV) that is analysed with thermoregulation images through Semantic Segmentation (SS). A novel methodology was adapted, combining thermoregulation imaging with SS using the U-Net++ model to investigate temperature distributions at the skin level. This work introduces a novel method that utilizes MobileNetV2 as the encoder for fast Feature Extraction (FE). Results: The results from the suggested model, achieves a segmentation accuracy of 95%, which is significantly more compared to that of DeepLabV3+ and PSPNet models. A mean and Intersection over Union (IoU) of 85% and 87% was reported by the suggested frameworks throughout the training and validation phases. Conclusion: Classifying normal and abnormal regions can be done via the outcomes, as it offers the great visibility in the thermal image for clinicians by detecting the non-thermal regions

Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:dbk:datame:v:3:y:2024:i::p:.367:id:1056294dm2024367

DOI: 10.56294/dm2024.367

Access Statistics for this article

More articles in Data and Metadata from AG Editor
Bibliographic data for series maintained by Javier Gonzalez-Argote ().