Plantar Thermogram Database for the Study of Diabetic Foot Complications

Hernandez-Contreras, D.; Peregrina-Barreto, Hayde; Rangel-Magdaleno, José; Renero-Carrillo, Francisco Javier

doi:10.1109/access.2019.2951356

Cited by 54 publications

(71 citation statements)

References 52 publications

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“…To test the binary classifier, thermograms of individuals with and without DM were obtained from a public database that contains 334 individual plantar thermograms, corresponding to 167 individuals (122 diabetic and 45 healthy) [ 23 ] (Table 1 ). Each thermogram, that corresponds to the plant of the left or right feet, is already segmented and vertically adjusted.…”

Section: Resultsmentioning

confidence: 99%

“…Table 3 illustrates the index values obtained for seven thermograms from the dataset (CG027, CG002, DM102, DM004, DM032, DM027, DM006 in [ 23 ]), the first two are from the control group and the remaining five from the DM group.…”

Section: Resultsmentioning

confidence: 99%

“…Considering the temperature of the entire foot it is possible to carry out a complete analysis of its general condition. However, as the foot does not have an uniform temperature, it is important to consider a regional division [ 23 ]. For example, in [ 24 , 25 ], temperatures were recorded in thirty-three regions of interest (ROIs) on both feet (considering the points of the foot that are most likely to ulcer) before the analysis.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Clustering Approach for Prediction of Diabetic Foot Using Thermal Images

Filipe

Teixeira

Teixeira³

2020

Computational Science and Its Applications – ICCSA 2020

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Diabetes Mellitus (DM) is one of the most predominant diseases in the world, causing a high number of deaths. Diabetic foot is one of the main complications observed in diabetic patients, which can lead to the development of ulcers. As the risk of ulceration is directly linked to an increase of the temperature in the plantar region, several studies use thermography as a method for automatic identification of problems in diabetic foot. As the distribution of plantar temperature of diabetic patients do not follow a specific pattern, it is difficult to measure temperature changes and, therefore, there is an interest in the development of methods that allow the detection of these abnormal changes. The objective of this work is to develop a methodology that uses thermograms of the feet of diabetic and healthy individuals and analyzes the thermal changes diversity in the plantar region, classifying each foot as belonging to a DM or a healthy individual. Based on the concept of clustering, a binary classifier to predict diabetic foot is presented; both a quantitative indicator and a classification thresholder (evaluated and validated by several performance metrics) are presented. To measure the binary classifier performance, experiments were conducted on a public dataset (with 122 images of DM individuals and 45 of healthy ones), being obtained the following metrics: Sensitivity = 0.73, Fmeasure = 0.81 and AUC = 0.84.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Clustering Approach for Prediction of Diabetic Foot Using Thermal Images

Filipe

Teixeira

Teixeira³

2020

Computational Science and Its Applications – ICCSA 2020

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“…This section describes all the steps followed to obtain a final comparison of the machine learning-based classifiers mentioned above. For purposes of this work, 110 thermograms of DM subjects obtained from a public thermogram database were used [53].…”

Section: Methodsmentioning

confidence: 99%

Deep Learning Classification for Diabetic Foot Thermograms

Cruz-Vega

Hernandez-Contreras

Peregrina-Barreto

et al. 2020

Sensors

Self Cite

100

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According to the World Health Organization (WHO), Diabetes Mellitus (DM) is one of the most prevalent diseases in the world. It is also associated with a high mortality index. Diabetic foot is one of its main complications, and it comprises the development of plantar ulcers that could result in an amputation. Several works report that thermography is useful to detect changes in the plantar temperature, which could give rise to a higher risk of ulceration. However, the plantar temperature distribution does not follow a particular pattern in diabetic patients, thereby making it difficult to measure the changes. Thus, there is an interest in improving the success of the analysis and classification methods that help to detect abnormal changes in the plantar temperature. All this leads to the use of computer-aided systems, such as those involved in artificial intelligence (AI), which operate with highly complex data structures. This paper compares machine learning-based techniques with Deep Learning (DL) structures. We tested common structures in the mode of transfer learning, including AlexNet and GoogleNet. Moreover, we designed a new DL-structure, which is trained from scratch and is able to reach higher values in terms of accuracy and other quality measures. The main goal of this work is to analyze the use of AI and DL for the classification of diabetic foot thermograms, highlighting their advantages and limitations. To the best of our knowledge, this is the first proposal of DL networks applied to the classification of diabetic foot thermograms. The experiments are conducted over thermograms of DM and control groups. After that, a multi-level classification is performed based on a previously reported thermal change index. The high accuracy obtained shows the usefulness of AI and DL as auxiliary tools to aid during the medical diagnosis.

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“…In addition, a number of e-health applications have proposed the use of IR thermography [9,17]. In the case of a diabetic foot, a database already exists to promote early diagnosis for this pathology [18]. The possibility of uploading self-monitoring images to a database has a twofold benefit.…”

Section: Introductionmentioning

confidence: 99%

Performance Assessment of Low-Cost Thermal Cameras for Medical Applications

Villa

Arteaga-Marrero

Ruiz-Alzola

2020

Sensors

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Thermal imaging is a promising technology in the medical field. Recent developments in low-cost infrared (IR) sensors, compatible with smartphones, provide competitive advantages for home-monitoring applications. However, these sensors present reduced capabilities compared to more expensive high-end devices. In this work, the characterization of thermal cameras is described and carried out. This characterization includes non-uniformity (NU) effects and correction as well as the thermal cameras’ dependence on room temperature, noise-equivalent temperature difference (NETD), and response curve stability with temperature. Results show that low-cost thermal cameras offer good performance, especially when used in temperature-controlled environments, providing evidence of the suitability of such sensors for medical applications, particularly in the assessment of diabetic foot ulcers on which we focused this study.

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Plantar Thermogram Database for the Study of Diabetic Foot Complications

Cited by 54 publications

References 52 publications

A Clustering Approach for Prediction of Diabetic Foot Using Thermal Images

A Clustering Approach for Prediction of Diabetic Foot Using Thermal Images

Deep Learning Classification for Diabetic Foot Thermograms

Performance Assessment of Low-Cost Thermal Cameras for Medical Applications

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