A Comparative Study of Deep Learning Algorithms for Detecting Fungal Infection Skin Diseases

Abstract

Many people place a high value on the health of their skin, frequently spending large sums of money on skincare products. Fungal infections are one of the most common skin conditions that can damage a person's self-esteem. When dealing with skin health issues, seeking advice from a knowledgeable dermatologist is essential. Deep learning is a contemporary technique that saves doctors time and helps them spot diseases early. Two deep learning algorithms that are useful in identifying patterns of skin illnesses are Mask R-CNN and YOLOv5. This paper explores using Mask R-CNN and YOLOv5 to recognize skin illnesses caused by fungal infections, going through several processing phases. The research results show that the YOLOv5 strategy performed best in accuracy, recall, precision, F1-Score, and AUC. This algorithm shows great potential and warrants further investigation in practical applications.

Author Biography

Joko Triloka, Institute of Informatics and Business Darmajaya

Faculty of Computer Science

Authors

  • Fajar Masya Mercu Buana University
  • Joko Triloka Institute of Informatics and Business Darmajaya
  • Setia Wulandari Institute of Informatics and Business Darmajaya

DOI:

https://doi.org/10.31449/inf.v49i16.6639

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Published

03/11/2025

How to Cite

Masya, F., Triloka, J., & Wulandari, S. (2025). A Comparative Study of Deep Learning Algorithms for Detecting Fungal Infection Skin Diseases. Informatica, 49(16). https://doi.org/10.31449/inf.v49i16.6639

Issue

Vol. 49 No. 16 (2025): Online-only issue

Section

Online-only

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