Dermatophytic Dynamics: A Holistic Approach to Superficial Mycosis Research

Human fungus infections are a serious public health issue that needs a prompt and correct diagnosis to cure effectively.

Human fungal infections have historically been treated with in-person consultations and exams by trained laboratory mycologists.

Lately, deep learning models-based computer-aided diagnosis systems have become more potent instruments, especially for late-stage mycological diagnoses.

This research presents experimental results using a deep learning technique to classify different species of fungi.

The work is noteworthy for using a vision transformer, demonstrating its effectiveness in classifying fungi.

This work contributes to the ongoing advancement of diagnostic techniques and provides a feasible route for artificial intelligence integration in mycology.

The results show a remarkable 97.3% accuracy rate, underscoring the potential of deep learning models—especially vision transformers—to enhance the efficacy and accuracy of fungal infection diagnosis.

This study contributes to the growing body of research on artificial intelligence and medical mycology.

The vision transformer’s efficacy in classifying fungi confirms the transformative potential of deep learning in diagnostic methods.