Deep Convolutional Neural Networks for Onychomycosis Detection
This work addresses the need for more efficient and accurate diagnosis of superficial fungal infections in dermatology, which is currently reliant on time-consuming and variable manual methods, representing an incremental improvement through automation of an existing clinical process.
The study tackled the problem of manual diagnosis of onychomycosis (fungal nail infection) by developing deep convolutional neural networks (VGG16 and InceptionV3) to automatically detect fungi in grayscale microscopic images, achieving high accuracy (e.g., 95.98% for VGG16) and AUC (0.9930) compared to clinicians' average accuracy of 72.8% and AUC of 0.87.
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.