A Deep Learning Model for Black Fungus Disease Identification Based on Optimization Techniques
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. This study presents a deep learning framework for the automated detection of mucormycosis infections from clinical imaging data. We propose a lightweight yet high-accuracy framework for image-based detection of mucormycosis that couples a pretrained MobileNetV2 backbone with a compact classification head whose key hyperparameters are tuned via Salp Swarm Optimization (SSO). The pipeline standardizes inputs to 224×224 RGB with ImageNet normalization, uses MobileNetV2 as a frozen feature extractor, and lets SSO search the head width uuu, dropout ppp, and learning rate η\etaη under early stopping. On a curated binary dataset (2,991 training / 747 validation images), the SSO search reached a peak validation accuracy of 99.87%, and the final model retrained with the best setting achieved 99.73% validation accuracy. The classification report shows near-perfect performance (diseased: precision/recall/F1 1.00; normal: precision/recall/F1 0.99), with an error rate of ≈0.27% (2/747) reflected in the confusion matrix. Against strong baselines—CNN (90.5%), VGG16 (95.0%), VGG19 (89.3%), InceptionV3 (97.9%)—MobileNetV2 + SSO ranks first while remaining computationally efficient. Grad-CAM visualizations confirm attention on peri-orbital and peri-lesional structures, supporting clinical plausibility. These results indicate that SSO-tuned MobileNetV2 offers state-of-the-art accuracy, interpretability, and deployment readiness for rapid mucormycosis screening.
Volume & Issue
Vol. Volume 21 / Iss. Issue 1