# Deep Convolutional Neural Networks for Onychomycosis Detection

arXiv: https://arxiv.org/abs/2106.16139 · HTML: https://mlsanity.com/paper/2106.16139/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 30 references that are in the map.
- First posted on arXiv 30 Jun 2021.

## Builds on (most cited first, 4 of 4)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?](https://mlsanity.com/paper/1511.06348/) (2015)
- [Comparing deep neural networks against humans: object recognition when the signal gets weaker](https://mlsanity.com/paper/1706.06969/) (2017)
- [Do We Need More Training Data?](https://mlsanity.com/paper/1503.01508/) (2015)

## Built on by (most cited first, 1 of 1)

- [Artefact-Aware Fungal Detection in Dermatophytosis: A Real-Time Transformer-Based Approach for KOH Microscopy](https://mlsanity.com/paper/2602.19156/) (2026)

## Abstract (arXiv)

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.