A Quantitative Model of Cutaneous Melanoma Diagnosis Using Thermography

Agyingi, Ephraim; Wiandt, Tamas; Maggelakis, Sophia

doi:10.1007/978-3-319-30379-6_16

Cited by 5 publications

(10 citation statements)

References 17 publications

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“…The active thermal imaging method proposed, displayed a low efficiency in the detection of ES I and CL II, being its identification only possible, when combined with the phase images acquired from the thermal signals. Steady‐state thermography was also successfully used to distinguish between benign lesions and melanomas with no vasculature, depending on the metabolic rate and tumour size selected for the theoretical model.…”

Section: Resultsmentioning

confidence: 99%

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Recent use of medical infrared thermography in skin neoplasms

Magalhães

Vardasca

Mendes

2018

Skin Research and Technology

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Section: Resultsmentioning

confidence: 99%

“…Upcoming research, related to the study of different modulation frequencies in LIT is appointed as necessary for the diagnosis of stage I and II melanoma . Moreover, the investigation of different tumour sizes, locations and varying blood vessel sizes could also be of interest, for future work …”

Section: Discussion Of Trends and Future Challengesmentioning

confidence: 99%

Recent use of medical infrared thermography in skin neoplasms

Magalhães

Vardasca

Mendes

2018

Skin Research and Technology

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“…Nonetheless, the developed methodology allowed the retrieval of static and dynamic thermal measurements that better characterize the skin lesion, instead of sustaining its diagnosis on a single thermogram visual assessment or a simple temperature measurement . Even though with a more intricate image analysis process, the distinctive parameters here included are still much simpler and directly acquired than the ones described on theoretical approaches …”

Section: Resultsmentioning

confidence: 99%

“…18 Even though with a more intricate image analysis process, the distinctive parameters here included are still much simpler and directly acquired than the ones described on theoretical approaches. [11][12][13][14]…”

Section: Machine Learning Classifiersmentioning

confidence: 99%

“…9 The use of IRT for the specific differentiation of melanoma and nevi lesions is a topic of research with unexploited potential, 10 since most recent publications focus on theoretical approaches for melanoma detection. [11][12][13][14][15][16] Articles concerning the clinical application of this technique present results based on populations little as one subject 17 and/or the sole implementation of a static methodology 18 with extremely basic image analysis strategies.…”

Section: Introductionmentioning

confidence: 99%

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Distinguishing melanocytic nevi from melanomas using static and dynamic infrared thermal imaging

Magalhães

Vardasca

Rebelo

et al. 2019

Acad Dermatol Venereol

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Background The incidence rates of melanoma have risen to worrying levels over the last decade. Delayed diagnosis, due to faults on the detection stage, indicates the necessity of new aiding diagnosis techniques. Since metabolic activity is highly connected to neoplasia formation, a detection technique that focuses its results on vascular responses, as Infrared thermal (IRT), seems to be a viable option. Materials and methods Static and dynamic (cooling) thermal images of melanoma and melanocytic nevi lesions were collected and analysed to retrieve thermal parameters characteristic of this skin lesion types. The steady‐state and dynamic variables were tested separately with different machine learning classifiers to verify whether the distinction of melanoma and nevi lesions was achievable. Results The differentiation of both types of skin tumours was doable, achieving an accuracy of 84.2% and a sensitivity of 91.3% with the implementation of a learner based on support vector machines and an input vector composed by static variables. Conclusion The use of IRT for skin tumour classification is achievable, but some improvement is needed to raise the metrics of sensitivity and specificity. For future work, it is recommended the study of dynamic parameters for the classification of other types of skin neoplasia.

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