Abstract
Current Status of Computational Intelligence Applications in Dermatological Clinical Practice
Carmen Rodríguez-Cerdeira*, José Luís González-Cespón and Roberto Arenas
Corresponding Author: Carmen Rodríguez-Cerdeira, Department of Dermatology, Hospital do Meixoeiro and University of Vigo, Vigo, Spain.
Accepted: March 02, 2021 Available Online: March 14, 2021
Citation: Rodríguez-Cerdeira C, González-Cespón JL & Arenas R. (2021) Current Status of Computational Intelligence Applications in Dermatological Clinical Practice. Dermatol Clin Res, S(1): 02.
Copyrights: ©2021 Rodríguez-Cerdeira C, González-Cespón JL & Arenas R. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Background: The yeast infections are increasingly frequent and the correct diagnosis consists of the identification of the yeast fungus, which in our case we are going to refer to the different species of Candida. The prescription of a broad-spectrum antifungal without taking into account the etiological agent, leads to an increase in the resistance to these treatments.

Objective: The objective of this work is to differentiate Candida albicans from other Candida species (Candida spp.) By means of digital images obtained from the optical microscope.

Material and Methods: It has reviewed about 100 photographs from patients in our consultations. In this study we will use the microscopic images of the Candida variety to be processed later with the Octave programming language and its image processing package (image-2.8.0).

Results and Discussion: This system is able to differentiate Candida albicans from the other varieties of Candida such as C. parapsilosis, C. krusei, and C. kefyr with accuracy. The candida identifier application, which was designed and programmed in Octave, allows identification of candidaspecies by locating certain geometric descriptors, such as the centroid and the surfaces of circular objects within the images. The program was highly effective for the diagnosis of Candida spp. So, we got a sensitivity and specificity above 90% with the images used.

Conclusion: The results that we obtain from the Candida spp. identifier system. that opens the way to be able to work with images obtained from the optical microscope.

Keywords: Candida spp, Octave, Image processing, Candydos program, Computational intelligence, Clinical practice, Candida infections