vgg19-defungi: a novel approach for direct fungal infection detection using vgg19 and microscopic images

Fungal infections, capable of establishing in various tissues and organs, are responsible for many human diseases that can lead to serious complications. the initial step in diagnosing fungal infections typically involves the examination of microscopic images. direct microscopic examination using potassium hydroxide is commonly employed as a screening method for diagnosing superficial fungal infections. although this type of examination is quicker than other diagnostic methods, the evaluation of a complete sample can be time-consuming. moreover, the diagnostic accuracy of these methods may vary depending on the skill of the practitioner and does not guarantee full reliability. this paper introduces a novel approach for diagnosing fungal infections using a modified vgg19 deep learning architecture. the method incorporates two significant changes: replacing the flatten layer with global average pooling (gap) to reduce feature count and model complexity, thereby enhancing the extraction of significant features from images. additionally, a dense layer with 1024 neurons is added post-gap, enabling the model to better learn and integrate these features. the defungi microscopic dataset was used for training and evaluating the model. the proposed method can identify fungal diseases with an accuracy of 97%, significantly outperforming the best existing method, which achieved an accuracy of 92.49%. this method not only significantly outperforms existing methods, but also, given its high accuracy, is valuable in the field of diagnosing fungal infections. this work demonstrates that the use of deep learning in diagnosing fungal diseases can lead to a substantial improvement in the quality of health services.