Multispectral imaging methods for the diagnosis of skin cancer lesions

En col·laboració amb la Universitat Autònoma de Barcelona (UAB) i la Universitat de Barcelona (UB).Skin cancer is the most prevalent form of cancer, and melanoma is one of the most threat disease of it. But it can be cured if it is detected early enough. Multispectral imaging is a potential method to differenciate melanoma from nevi as it provides spectral images with information of absorbance and reflectance. With this aim, spectral images along the visible and near infrared range (from 415nm to 995nm) of 165 lesions including nevi, melanomas and basal cell carcinomas were processed in this master thesis. After obtaining all data in terms of reflectance and absorbance and other related parameters for each pixel of the segmented lesions, a statistical analysis was carried out to quantify their spatial distribution all over each lesion. Algorithms such as Support vector machine (SVM) and Discriminant Analysis (DA) were used as a means of classifying the lesions. The results show that DA linear classifier provides a better diagnosis than the SVM. BCCs are easier to discriminate from nevi than melanomas

Multispectral imaging methods for the diagnosis of skin cancer lesions

En col·laboració amb la Universitat Autònoma de Barcelona (UAB) i la Universitat de Barcelona (UB).Skin cancer is the most prevalent form of cancer, and melanoma is one of the most threat disease of it. But it can be cured if it is detected early enough. Multispectral imaging is a potential method to differenciate melanoma from nevi as it provides spectral images with information of absorbance and reflectance. With this aim, spectral images along the visible and near infrared range (from 415nm to 995nm) of 165 lesions including nevi, melanomas and basal cell carcinomas were processed in this master thesis. After obtaining all data in terms of reflectance and absorbance and other related parameters for each pixel of the segmented lesions, a statistical analysis was carried out to quantify their spatial distribution all over each lesion. Algorithms such as Support vector machine (SVM) and Discriminant Analysis (DA) were used as a means of classifying the lesions. The results show that DA linear classifier provides a better diagnosis than the SVM. BCCs are easier to discriminate from nevi than melanomas

Hollow Graphitized Carbon Nanocage Supported Pd Catalyst with Excellent Electrocatalytic Activity for Ethanol Oxidation

Low cost, high activity and reliable stability are significant to the commercialization of fuel cell electrocatalysts. However, the synthesis of non-Pt anode catalysts with low cost, excellent performance and reliable stability is still a great challenge. Herein, we developed hollow graphitized carbon nanocages for improving the electrocatalytic performance of Pd nanoparticles (NPs) toward ethanol oxidation. A mild method was utilized for the preparation of hollow graphitized carbon nanocages (CN) using magnesium oxide as a sacrificial template without high-temperature processing. The CN can act as high-efficiency support for the distribution of Pd NPs. Pd NPs decorated on CN exhibited high catalytic performance with the current density of 2411.5 mA mg^–1 for ethanol oxidation reaction, which is 1.84 and 4.42 times higher than reduced graphene oxide (1308.5 mA mg^–1) and C (545.2 mA mg^–1) as supports, respectively. The Pd/CN with excellent catalytic performance can be attributed to the CN, including the large surface area with a mesoporous hollow structure, uniform dispersion of Pd NPs, and excellent electrical conductivity. This study may offer new insights for the development of highly effective carbon-based support for applications in ethanol oxidation