Journal
JOURNAL OF CLINICAL MEDICINE
Volume 11, Issue 9, Pages -Publisher
MDPI
DOI: 10.3390/jcm11092315
Keywords
basal cell carcinoma; hyperspectral sensor; computational learning; convolutional neural networks; support vector machines
Categories
Funding
- Junta de Castilla y Leon [GRS 1837/A/18]
- Spanish Ministry of Science, Innovation and Universities [PRE2019-089411, RTI2018-099850-B-I00]
- University of Salamanca
- Iberdrola Spain through the initiative Catedra Iberdrola VIII Centenario of the University of Salamanca
- Instituto de Salud Carlos III [PI18/00587]
- FEDER
- Gerencia Regional de Salud de Castilla y Leon [GRS 2139/A/20]
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Non-melanoma skin cancer, particularly basal cell carcinoma, is a common type of cancer. Early detection is vital, and the combination of multispectral imaging and artificial intelligence offers a non-invasive method for detection and classification with high accuracy.
Non-melanoma skin cancer, and basal cell carcinoma in particular, is one of the most common types of cancer. Although this type of malignancy has lower metastatic rates than other types of skin cancer, its locally destructive nature and the advantages of its timely treatment make early detection vital. The combination of multispectral imaging and artificial intelligence has arisen as a powerful tool for the detection and classification of skin cancer in a non-invasive manner. The present study uses hyperspectral images to discern between healthy and basal cell carcinoma hyperspectral signatures. Upon the combined use of convolutional neural networks, with a final support vector machine activation layer, the present study reaches up to 90% accuracy, with an area under the receiver operating characteristic curve being calculated at 0.9 as well. While the results are promising, future research should build upon a dataset with a larger number of patients.
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