Courtenay Lloyd A, González-Aguilera Diego, Lagüela Susana, Pozo Susana Del, Ruiz Camilo, Barbero-García Innes, Román-Curto Concepción, Cañueto Javier, Santos-Durán Carlos, Cardeñoso-Álvarez María Esther, Roncero-Riesco Mónica, Hernández-López David, Guerrero-Sevilla Diego, Rodríguez-Gonzalvez Pablo
Department of Cartographic and Terrain Engineering, Higher Polytechnic School of Ávila, University of Salamanca, Hornos Caleros 50, 05003 Ávila, Spain.
Department of Didactics of Mathematics and Experimental Sciences, Faculty of Education, Paseo de Canaleja 169, 37008 Salamanca, Spain.
J Clin Med. 2022 Apr 21;11(9):2315. doi: 10.3390/jcm11092315.
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.
非黑色素瘤皮肤癌，尤其是基底细胞癌，是最常见的癌症类型之一。尽管这种恶性肿瘤的转移率低于其他类型的皮肤癌，但其局部破坏性以及及时治疗的优势使得早期检测至关重要。多光谱成像与人工智能的结合已成为一种以非侵入性方式检测和分类皮肤癌的强大工具。本研究使用高光谱图像来辨别健康皮肤和基底细胞癌的高光谱特征。通过结合使用卷积神经网络以及最终的支持向量机激活层，本研究的准确率高达90%，同时计算出的受试者工作特征曲线下面积也为0.9。虽然结果很有前景，但未来的研究应以包含更多患者的数据集为基础。
