Journal
JOURNAL OF CLINICAL MEDICINE
Volume 11, Issue 15, Pages -Publisher
MDPI
DOI: 10.3390/jcm11154392
Keywords
Non-Melanoma Skin Cancer; Elliptic Fourier Analysis; shape analysis; skin lesion asymmetry; clinical images; computer vision
Categories
Funding
- European Regional Development Fund
- Junta de Castilla y Leon, under the project name DETECCTHIA [SA097P20]
- Spanish Ministry of Science, Innovation, and Universities [PRE2019-089411, RTI2018-099850-B-I00]
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This study analyzed a dataset of images containing different types of NMSC and benign skin lesions, and found that shape is an important parameter for NMSC detection. The combination of EFA coefficients and asymmetry showed notable success rates in classifying benign and malignant lesions.
The early detection of Non-Melanoma Skin Cancer (NMSC) is crucial to achieve the best treatment outcomes. Shape is considered one of the main parameters taken for the detection of some types of skin cancer such as melanoma. For NMSC, the importance of shape as a visual detection parameter is not well-studied. A dataset of 993 standard camera images containing different types of NMSC and benign skin lesions was analysed. For each image, the lesion boundaries were extracted. After an alignment and scaling, Elliptic Fourier Analysis (EFA) coefficients were calculated for the boundary of each lesion. The asymmetry of lesions was also calculated. Then, multivariate statistics were employed for dimensionality reduction and finally computational learning classification was employed to evaluate the separability of the classes. The separation between malignant and benign samples was successful in most cases. The best-performing approach was the combination of EFA coefficients and asymmetry. The combination of EFA and asymmetry resulted in a balanced accuracy of 0.786 and an Area Under Curve of 0.735. The combination of EFA and asymmetry for lesion classification resulted in notable success rates when distinguishing between benign and malignant lesions. In light of these results, skin lesions' shape should be integrated as a fundamental part of future detection techniques in clinical screening.
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