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Skin lesion detection algorithms in whole body images
- Source :
- Sensors, Volume 21, Issue 19, Sensors, Vol 21, Iss 6639, p 6639 (2021), Sensors (Basel, Switzerland)
- Publication Year :
- 2021
-
Abstract
- Melanoma is one of the most lethal and rapidly growing cancers, causing many deaths each year. This cancer can be treated effectively if it is detected quickly. For this reason, many algorithms and systems have been developed to support automatic or semiautomatic detection of neoplastic skin lesions based on the analysis of optical images of individual moles. Recently, full-body systems have gained attention because they enable the analysis of the patient’s entire body based on a set of photos. This paper presents a prototype of such a system, focusing mainly on assessing the effectiveness of algorithms developed for the detection and segmentation of lesions. Three detection algorithms (and their fusion) were analyzed, one implementing deep learning methods and two classic approaches, using local brightness distribution and a correlation method. For fusion of algorithms, detection sensitivity = 0.95 and precision = 0.94 were obtained. Moreover, the values of the selected geometric parameters of segmented lesions were calculated and compared for all algorithms. The obtained results showed a high accuracy of the evaluated parameters (error of area estimation &lt<br />10%), especially for lesions with dimensions greater than 3 mm, which are the most suspected of being neoplastic lesions.
- Subjects :
- Skin Neoplasms
Computer science
skin lesion detection
TP1-1185
Skin Diseases
Biochemistry
Article
Analytical Chemistry
algorithm fusion
Body Image
Humans
Segmentation
Sensitivity (control systems)
Electrical and Electronic Engineering
Melanoma
Instrumentation
business.industry
Deep learning
Chemical technology
whole body system
Atomic and Molecular Physics, and Optics
Correlation method
Artificial intelligence
Whole body
business
Skin lesion
Algorithm
Algorithms
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Sensors, Volume 21, Issue 19, Sensors, Vol 21, Iss 6639, p 6639 (2021), Sensors (Basel, Switzerland)
- Accession number :
- edsair.doi.dedup.....a3b942e1eb031bc2cae5cbc9d0322b35