Neural Networks-Based On-Site Dermatologic Diagnosis through Hyperspectral Epidermal Images.

ABSTRACT: Cancer originates from the uncontrolled growth of healthy cells into a mass. Chromophores, such as hemoglobin and melanin, characterize skin spectral properties, allowing the classification of lesions into different etiologies. Hyperspectral imaging systems gather skin-reflected and transmitted light into several wavelength ranges of the electromagnetic spectrum, enabling potential skin-lesion differentiation through machine learning algorithms. Challenged by data availability and tiny inter and intra-tumoral variability, here we introduce a pipeline based on deep neural networks to diagnose hyperspectral skin cancer images, targeting a handheld device equipped with a low-power graphical processing unit for routine clinical testing. Enhanced by data augmentation, transfer learning, and hyp

SUBMITTER: La Salvia M 

PROVIDER: S-EPMC9571453 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature