2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 21 , Issue 2 , PP: 458-475, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Hanan Badri Salman 1 * , Matheel Emaduldeen Abdulmunim 2
Doi: https://doi.org/10.54216/FPA.210228
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.
Black Fungus Disease Identification , COVID-19 , deep learning , VGG16 , VGG19 , Inception , MobileNet
[1] K. M. M. Huq, M. G. Hossain, M. S. Islam, M. A. Sobur, A. M. M. T. Rahman, and M. T. Rahman, “Mucormycosis (black fungus) and its impact on the COVID-19 patients: An updated review,” Journal of Advanced Biotechnology and Experimental Therapeutics, vol. 5, no. 1, pp. 198–217, 2022, doi: 10.5455/JABET.2022.D108.
[2] Mousavi, M. T. Hedayati, N. Hedayati, M. Ilkit, and S. Syedmousavi, “Aspergillus species in indoor environments and their possible occupational and public health hazards,” Curr Med Mycol, vol. 2, no. 1, p. 36, Mar. 2016, doi: 10.18869/ACADPUB.CMM.2.1.36.
[3] R. Thornton, “Detection of the ‘Big Five’ mold killers of humans: Aspergillus, Fusarium, Lomentospora, Scedosporium and Mucormycetes,” Adv Appl Microbiol, vol. 110, pp. 1–61, Jan. 2020, doi: 10.1016/bs.aambs.2019.10.003.
[4] P. Dam et al., “Surge of mucormycosis during the COVID-19 pandemic,” Travel Med Infect Dis, vol. 52, p. 102557, Mar. 2023, doi: 10.1016/J.TMAID.2023.102557.
[5] S. Dogra et al., “Mucormycosis Amid COVID-19 Crisis: Pathogenesis, Diagnosis, and Novel Treatment Strategies to Combat the Spread,” Front Microbiol, vol. 12, p. 794176, Jan. 2022, doi: 10.3389/FMICB.2021.794176/FULL.
[6] K. Tayabali, H. Pothiwalla, and S. Narayanan, “Epidemiology of COVID-19–Associated Mucormycosis,” Curr Fungal Infect Rep, vol. 17, no. 2, pp. 156–175, Jun. 2023, doi: 10.1007/S12281-023-00464-2/TABLES/1.
[7] P. Monika and M. N. Chandraprabha, “Risks of mucormycosis in the current Covid-19 pandemic: a clinical challenge in both immunocompromised and immunocompetent patients,” Mol Biol Rep, vol. 49, no. 6, pp. 4977–4988, Jun. 2022, doi: 10.1007/S11033-022-07160-3/FIGURES/3.
[8] R. M. Anjana et al., “Prevalence of diabetes and prediabetes (impaired fasting glucose and/or impaired glucose tolerance) in urban and rural India: Phase i results of the Indian Council of Medical Research-INdia DIABetes (ICMR-INDIAB) study,” Diabetologia, vol. 54, no. 12, pp. 3022–3027, Dec. 2011, doi: 10.1007/S00125-011-2291-5/FIGURES/1.
[9] S. Kapoor, P. K. Saidha, A. Gupta, U. Saini, and S. Satya, “COVID-19 Associated Mucormycosis with Newly Diagnosed Diabetes Mellitus in Young Males - A Tertiary Care Experience,” Int Arch Otorhinolaryngol, vol. 26, no. 3, pp. 470–477, Nov. 2022, doi: 10.1055/S-0042-1748927.
[10] M. Kumar et al., “Mucormycosis in COVID-19 pandemic: Risk factors and linkages,” Curr Res Microb Sci, vol. 2, p. 100057, Dec. 2021, doi: 10.1016/J.CRMICR.2021.100057.
[11] H. Gogineni, W. So, K. Mata, and J. N. Greene, “Multidisciplinary approach in diagnosis and treatment of COVID-19-associated mucormycosis: a description of current reports,” Egypt J Intern Med, vol. 34, no. 1, p. 58, Dec. 2022, doi: 10.1186/S43162-022-00143-7.
[12] B. Spellberg, T. J. Walsh, D. P. Kontoyiannis, J. J. Edwards, and A. S. Ibrahim, “Recent Advances in the Management of Mucormycosis: From Bench to Bedside,” Clin Infect Dis, vol. 48, no. 12, p. 1743, Jun. 2009, doi: 10.1086/599105.
[13] T. Suo, M. Xu, and Q. Xu, “Clinical characteristics and mortality of mucormycosis in hematological malignancies: a retrospective study in Eastern China,” Ann Clin Microbiol Antimicrob, vol. 23, no. 1, p. 82, Dec. 2024, doi: 10.1186/S12941-024-00738-8.
[14] J. Safiia et al., “Recent Advances in Diagnostic Approaches for Mucormycosis,” Journal of Fungi, vol. 10, no. 10, p. 727, Oct. 2024, doi: 10.3390/JOF10100727.
[15] Sharma and A. Goel, “Mucormycosis: risk factors, diagnosis, treatments, and challenges during COVID-19 pandemic,” Folia Microbiol (Praha), vol. 67, no. 3, p. 363, Jun. 2022, doi: 10.1007/S12223-021-00934-5.
[16] S. Jafari et al., “Diagnostic Challenges in Fungal Coinfections Associated With Global COVID-19,” Scientifica (Cairo), vol. 2025, no. 1, p. 6840605, Jan. 2025, doi: 10.1155/SCI5/6840605.
[17] O. A. Barbosa, E. S. do Amaral, G. P. Furtado, V. C. F. da Silva, I. M. de Alencar, and K. A. de Freitas, “FUNGAL NECROTIZING FASCIITIS DUE TO MUCORMYCOSIS FOLLOWING CONTAMINATED SUBSTANCE INOCULATION: A REPORT OF TWO CASES,” Eur J Case Rep Intern Med, vol. 11, no. 11, 2024, doi: 10.12890/2024_004914.
[18] Skiada, C. Lass-Floerl, N. Klimko, A. Ibrahim, E. Roilides, and G. Petrikkos, “Challenges in the diagnosis and treatment of mucormycosis,” Med Mycol, vol. 56, pp. S93–S101, Apr. 2018, doi: 10.1093/MMY/MYX101.
[19] B. Olawade, O. Z. Wada, A. Odetayo, A. C. David-Olawade, F. Asaolu, and J. Eberhardt, “Enhancing mental health with Artificial Intelligence: Current trends and future prospects,” Journal of Medicine, Surgery, and Public Health, vol. 3, p. 100099, Aug. 2024, doi: 10.1016/J.GLMEDI.2024.100099.
[20] M. Khosravi, Z. Zare, S. M. Mojtabaeian, and R. Izadi, “Artificial Intelligence and Decision-Making in Healthcare: A Thematic Analysis of a Systematic Review of Reviews,” Health Serv Res Manag Epidemiol, vol. 11, p. 23333928241234864, Jan. 2024, doi: 10.1177/23333928241234863.
[21] S. Syed-Abdul et al., “Using artificial intelligence-based models to predict the risk of mucormycosis among COVID-19 survivors: An experience from a public hospital in India,” J Infect, vol. 84, no. 3, p. 351, Mar. 2021, doi: 10.1016/J.JINF.2021.12.016.
[22] Suvarna, N. Bappalige, and K. P. Karani, “Enhancing Medical Image Analysis with Convolutional Neural Networks: A Paradigm Shift in Healthcare Diagnostics,” pp. 133–139, 2024.
[23] Prinzi, C. Militello, Y. Matsuzaka, and R. Yashiro, “The Diagnostic Classification of the Pathological Image Using Computer Vision,” Algorithms, vol. 18, no. 2, p. 96, Feb. 2025, doi: 10.3390/A18020096.
[24] fatima, R. H. Allami, and M. G. Yousif, “Integrative AI-Driven Strategies for Advancing Precision Medicine in Infectious Diseases and Beyond: A Novel Multidisciplinary Approach,” Jul. 2023, Accessed: Aug. 06, 2025. [Online]. Available: http://arxiv.org/abs/2307.15228
[25] Nira and H. Kumar, “Epidemiological Mucormycosis treatment and diagnosis challenges using the adaptive properties of computer vision techniques based approach: a review,” Multimed Tools Appl, vol. 81, no. 10, pp. 14217–14245, Apr. 2022, doi: 10.1007/S11042-022-12450-W/METRICS.
[26] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-December, pp. 770–778, Dec. 2016, doi: 10.1109/CVPR.2016.90.
[27] D. Mienye, T. G. Swart, G. Obaido, M. Jordan, and P. Ilono, “Deep Convolutional Neural Networks in Medical Image Analysis: A Review,” Information (Switzerland), vol. 16, no. 3, Mar. 2025, doi: 10.3390/INFO16030195.
[28] M. Vyas, I. Sehgal, and E. Dannaoui, “Recent Developments in the Diagnosis of Mucormycosis,” Journal of Fungi, vol. 8, no. 5, p. 457, Apr. 2022, doi: 10.3390/JOF8050457.
[29] D. Dusa and M. R. Gundavarapu, “Smart Framework for Black Fungus Detection using VGG 19 Deep Learning Approach,” *8th International Conference on Advanced Computing and Communication Systems, ICACCS 2022*, pp. 1023–1028, 2022, doi: 10.1109/ICACCS54159.2022.9785123.
[30] M. I. Hasan, N. I. Mahbub, and B. Sarkar, “Identification of Black Fungus Diseases Using CNN and Transfer-Learning Approach,” ACM International Conference Proceeding Series, pp. 118–125, Mar. 2022, doi: 10.1145/3542954.3542972.
[31] S. Karthikeyan, G. Ramkumar, S. Aravindkumar, M. Tamilselvi, S. Ramesh, and A. Ranjith, “A Novel Deep Learning-Based Black Fungus Disease Identification Using Modified Hybrid Learning Methodology,” Contrast Media Mol Imaging, vol. 2022, 2022, doi: 10.1155/2022/4352730.
[32] P.Sri Lakshmi Durga and M . Kalidas, “BLACK FUNGUS DETECTIONUSING MACHINE LEARNING,” international journal of engineering technology and management sciences, vol. 6, no. 6, pp. 393–397, Nov. 2022, doi: 10.46647/IJETMS.2022.V06I06.070.
[33] G. S. Annie Grace Vimala, R. Kesavan, E. Manigandan, S. Pushpa Latha, B. V. Kumar, and S. Padmakala, “Black Fungus Infection Detection using AI-based Early Warning System for Patients through Multi-Modal Medical Imaging,” *2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023 - Proceedings*, pp. 1783–1789, 2023, doi: 10.1109/ICACRS58579.2023.10404938.
[34] P. V. S. Charan and G. Ramkumar, “Mucormycosis Detection using Hybrid Convolutional Neural Network with Support Vector Machine and Compare the performance with Support Vector Machine,” Proceedings of the International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering, ICECONF 2023, 2023, doi: 10.1109/ICECONF57129.2023.10083770.
[35] M. Abdul Hameed, M. S. U. Rahman, and A. Banu, “Black Fungus Prediction in Covid Contrived Patients Using Deep Learning,” Intelligent Systems Reference Library, vol. 231, pp. 309–321, 2023, doi: 10.1007/978-3-031-12419-8_16.
[36] R. Patil et al., “Development of a machine learning model to predict risk of development of COVID-19-associated mucormycosis,” Future Microbiol, vol. 19, no. 4, pp. 297–305, 2024, doi: 10.2217/FMB-2023-0190.
[37] E. Hassan, A. Saber, E. S. M. El-Kenawy, R. Bhatnagar, and M. Y. Shams, “Early Detection of Black Fungus Using Deep Learning Models for Efficient Medical Diagnosis,” Proceedings of the 2024 International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024, pp. 426–431, 2024, doi: 10.1109/ICETCI62771.2024.10704103.
[38] Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, Sep. 2014, Accessed: Aug. 06, 2025. [Online]. Available: https://arxiv.org/abs/1409.1556v6
[39] D. Apostolopoulos, N. D. Papathanasiou, N. Papandrianos, E. Papageorgiou, and D. J. Apostolopoulos, “Innovative Attention-Based Explainable Feature-Fusion VGG19 Network for Characterising Myocardial Perfusion Imaging SPECT Polar Maps in Patients with Suspected Coronary Artery Disease,” Applied Sciences, vol. 13, no. 15, p. 8839, Jul. 2023, doi: 10.3390/APP13158839.
[40] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 4510–4520, Jan. 2018, doi: 10.1109/CVPR.2018.00474.
[41] O. Iparraguirre-Villanueva, V. Guevara-Ponce, O. R. Paredes, F. Sierra-Liñan, J. Zapata-Paulini, and M. Cabanillas-Carbonell, “Convolutional Neural Networks with Transfer Learning for Pneumonia Detection,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 9, pp. 544–551, 2022, doi: 10.14569/IJACSA.2022.0130963.
[42] S. I. Saedi and M. Rezaei, “A Modified Xception Deep Learning Model for Automatic Sorting of Olives Based on Ripening Stages,” Inventions, vol. 9, no. 1, p. 6, Dec. 2023, doi: 10.3390/INVENTIONS9010006.
[43] M. Ennab and H. Mcheick, “Advancing AI Interpretability in Medical Imaging: A Comparative Analysis of Pixel-Level Interpretability and Grad-CAM Models,” Machine Learning and Knowledge Extraction, vol. 7, no. 1, p. 12, Feb. 2025, doi: 10.3390/MAKE7010012.
