Detection of Fungal Infections from Microscopic Fungal Images Using Deep Learning Techniques




Deep learning, DeFungi dataset, Fungal infections, Microscopic images


Fungal infections, due to their diverse manifestations and varying characteristics, present significant challenges in medical diagnosis. This study delves into applying deep-learning techniques for detecting fungal infections from microscopic fungal images. By harnessing the power of Convolutional Neural Networks (CNNs), we propose an approach that employs transfer learning to accurately classify different fungal species. The dataset comprises microscopic images of various fungal types, and to enhance model performance, we utilize data augmentation techniques. Furthermore, we aim to boost performance by fine-tuning the model's layers. Initially starting at 84.38% accuracy, our experimental results progressively reached high values of 95.35% and 97.19%. These results underscore the effectiveness of our deep learning approach in precisely identifying and classifying fungal infections. This success holds promising potential to aid medical professionals in timely and accurate diagnoses. The findings presented in this study contribute to ongoing research in medical image analysis and drive advancements in the field of automated disease detection.

Author Biography

Yavuz Selim TASPINAR, Selcuk University

Doganhisar Vocational School Selcuk University Konya, TÜRKİYE




How to Cite

CINAR, I., & TASPINAR, Y. S. (2023). Detection of Fungal Infections from Microscopic Fungal Images Using Deep Learning Techniques. Proceedings of the International Conference on Advanced Technologies, 11, 21–28.