Black Fungus Disease Identification Using Deep Learning: A Case Study

Hanan Badri Salman^1,*, Matheel Emaduldeen Abdulmunim²

¹Informatics Institute for Postgraduate Studies, Information Technology & Communications University, Baghdad, Iraq

²College of Computer Science, University of Technology, Baghdad, Iraq

Emails: ms202330748@iips.edu.iq; 110104@uotechnology.edu.iq

Abstract

Black fungus disease (mucormycosis) has emerged as a critical health threat, particularly during the COVID-19 pandemic, where immunosuppressed individuals have shown increased susceptibility to opportunistic fungal infections. The aggressive progression of mucormycosis and its high mortality rate, exacerbated by diagnostic delays, underscore the urgent need for accurate and automated detection systems. In this study, a deep learning-based diagnostic framework is proposed for the early identification of black fungus infection using convolutional neural networks (CNNs). Experimental pipelines were developed and evaluated. Several deep learning models based traditional CNN architectures including VGG16, VGG19, InceptionV3, and MobileNetV2 have been study on a structured dataset comprising high-resolution mucormycosis images. Comparative evaluations across both pipelines revealed that the MobileNetV2 architecture consistently outperformed other models, with accuracy reaching 99.86%, F1-score of 0.98, and minimal overfitting across validation datasets. The proposed system holds strong potential for real-world clinical deployment, particularly in resource-limited healthcare settings, offering rapid, scalable, and explainable AI-driven diagnostics to combat the rising threat of black fungus infections.

Keywords: Black Fungus Disease Identification; COVID-19; deep learning; VGG16; VGG19; Inception; MobileNet