  The diagnosis of superficial fungal infections in dermatology is still mostly
based on manual direct microscopic examination with Potassium Hydroxide (KOH)
solution. However, this method can be time consuming and its diagnostic
accuracy rates vary widely depending on the clinician's experience. With the
increase of neural network applications in the field of clinical microscopy, it
is now possible to automate such manual processes increasing both efficiency
and accuracy. This study presents a deep neural network structure that enables
the rapid solutions for these problems and can perform automatic fungi
detection in grayscale images without dyes. 160 microscopic field photographs
containing the fungal element, obtained from patients with onychomycosis, and
297 microscopic field photographs containing dissolved keratin obtained from
normal nails were collected. Smaller patches containing 4234 fungi and 4981
keratin were extracted from these images. In order to detect fungus and
keratin, VGG16 and InceptionV3 models were developed. The VGG16 model had
95.98% accuracy, and the area under the curve (AUC) value of 0.9930, while the
InceptionV3 model had 95.90% accuracy and the AUC value of 0.9917. However,
average accuracy and AUC value of clinicians is 72.8% and 0.87, respectively.
This deep learning model allows the development of an automated system that can
detect fungi within microscopic images.
