Proposing an ensemble learning model based on neural network and fuzzy system for keratoconus diagnosis based on Pentacam measurements.

PURPOSE: The present study was done to evaluate efficiency of an ensemble learning structure for automatic keratoconus diagnosis and to categorize eyes into four different groups based on a combination of 19 parameters obtained from Pentacam measurements. METHODS: Pentacam data from 450 eyes were enrolled in the study. Eyes were separated into training, validation, and testing sets. An ensemble system was used to analyze corneal measurements and categorize the eyes into four groups. The ensemble system was trained to consider indices from both anterior and posterior corneal surfaces. Efficiency of the ensemble system was evaluated and compared in each group. RESULTS: The best accuracy was achieved by the ensemble system with both multilayer perceptron and neuro-fuzzy system classifiers alongside the Naïve Bayes combination method. The accuracy achieved in KC versus N distinction task was equal to 98.2% with 99.1% of sensitivity and 96.2% of specificity for KC detection. The global accuracy was equal to 98.2% for classification of 4 groups, with an average sensitivity of 98.5% and specificity of 99.4%. CONCLUSION: In this study, authority of an ensemble learning system to work out intricate problems was presented. Despite using fewer parameters, herein, comparable or, in some cases, better results were obtained than methods reported in the literature. The proposed method demonstrated very good accuracy in discriminating between normal eyes and different stages of keratoconus eyes. In some cases, it was not possible to directly compare our results with the literature, due to differences in definitions of KC group as well as differences in selection of items and parameters.

Molecular identification and prevalence of malassezia species in pityriasis versicolor patients from kashan, iran.

BACKGROUND: Malassezia species are lipophilic yeasts found on the skin surface of humans and other warm-blooded vertebrates. It is associated with various human diseases, especially pityriasis versicolor, which is a chronic superficial skin disorder. OBJECTIVES: The aim of the present study was to identify Malassezia species isolated from patients' samples affected by pityriasis versicolor, using molecular methods in Kashan, Iran. PATIENTS AND METHODS: A total of 140 subjects, suspected of having pityriasis versicolor from Kashan, were clinically diagnosed and then confirmed by direct microscopic examination. The scraped skin specimens were inoculated in modified Dixon's medium. DNA was extracted from the colonies and PCR amplification was carried out for the 26s rDNA region. PCR products were used to further restriction fragment length polymorphism by CfoI enzyme. RESULTS: Direct examination was positive in 93.3% of suspected pityriasis versicolor lesions. No statistically significant difference was observed in the frequency of Malassezia species between women and men. The highest prevalence of tinea versicolor was seen in patients 21-30 years-of-age. No difference could be seen in the frequency of Malassezia species depending on the age of the patients. In total, 65% of patients with pityriasis versicolor had hyperhidrosis. The most commonly isolated Malassezia species in the pityriasis versicolor lesions were; Malassezia globosa (66%), M. furfur (26%), M. restricta (3%), M. sympodialis (3%), and M. slooffiae (2%). Malassezia species were mainly isolated from the neck and chest. CONCLUSIONS: This study showed M. globosa to be the most common Malassezia species isolated from Malassezia skin disorders in Kashan, Iran. The PCR-RFLP method was useful in the rapid identification of the Malassezia species. By using these methods, the detection and identification of individual Malassezia species from clinical samples was substantially easier.