The correct nomenclature of Zirelí sign in the propaedeutics of pityriasis versicolor (in memoriam)

Veasey, John Verrinder; Macedo, Priscila Marques de; Amorim, José Roberto; Orofino-Costa, Rosane

doi:10.1016/j.abd.2021.04.004

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…Also, the KeepProp (KP) is the unit operation probability during dropout state, as shown in Eq. (3).…”

Section: Detector Stagementioning

confidence: 99%

“…Infection from fungus is named "mycoses." There are two types of mycoses in humans: superficial mycoses infected from various species [3][4][5][6] and systemic mycoses [7][8][9][10]. There are primary process methods to classify the mycoses species, such as matrixassisted laser desorption/ionization time-of-flight mass spectrometry and polymerase chain reaction to assess the presence of other candida albicans and aspergillus under aerobic conditions [11], openended coaxial probe in couple with the microwave analysis to identify the fungus electrical properties [12].…”

Section: Introductionmentioning

confidence: 99%

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Scaled Dilation of DropBlock Optimization in Convolutional Neural Network for Fungus Classification

Prommakhot¹,

Srinonchat²

2022

Computers, Materials &Amp; Continua

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Image classification always has open challenges for computer vision research. Nowadays, deep learning has promoted the development of this field, especially in Convolutional Neural Networks (CNNs). This article proposes the development of efficiently scaled dilation of DropBlock optimization in CNNs for the fungus classification, which there are five species in this experiment. The proposed technique adjusts the convolution size at 35, 45, and 60 with the max-polling size 2 × 2. The CNNs models are also designed in 12 models with the different BlockSizes and KeepProp. The proposed techniques provide maximum accuracy of 98.30% for the training set. Moreover, three accurate models, called Precision, Recall, and F1-score, are employed to measure the testing set. The experiment results expose that the proposed models achieve to classify the fungus and provide an excellent accuracy compared with the previous techniques. Furthermore, the proposed techniques can reduce the CNNs structure layer, directly affecting resource and time computation.

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“…Also, the KeepProp (KP) is the unit operation probability during dropout state, as shown in Eq. (3).…”

Section: Detector Stagementioning

confidence: 99%