A fungus spores dataset and a convolutional neural network based approach for fungus detection

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Multi-Convolutional Neural Network-Based Diagnostic Software for the Presumptive Determination of Non-Dermatophyte Molds

Milanović, M., Otašević, S., Ranđelović, M., Grassi, A., Cafarchia, C., Mareș, M., & Milosavljević, A. (2024) · Electronics · doi:10.3390/electronics13030594 · Cited by 2 · see citing works

A Hybrid Classification Model (Fruits or Vegetable) Using Deep Learning Techniques

Maurya, K. K., Verma, A., Gaur, D., & Patel, A. (2024) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2024.63483 · Cited by 2 · see citing works

Revolutionizing Microbial Infection Diagnosis: The Role of Artificial Intelligence

Hossainpour, H., & Mahmoudi, H. (2024) · Iranian Journal of Medical Microbiology · doi:10.30699/ijmm.18.2.66 · Cited by 2 · see citing works

Innovative infrastructure to access Brazilian fungal diversity using deep learning

Chaves, T. Z. L., Xavier, J. S., dos Santos, A. G., Martins-Cunha, K., Karstedt, F., Kossmann, T., Sourell, S., Leopoldo, E., Ferreira, M. N. F., Farias, R., et al. (2024) · PeerJ · doi:10.7717/peerj.17686 · Cited by 2 · see citing works

AI-Powered Fungal Detection In Dried Chilli: A Deep Learning Approach

Indira Priyadharshini, M., Pavithra, R. H., Mahalakshmi, M. P., Mohanapriya, S., & Jasiya Yasika, S. (2024) · Asian Conference on Innovation in Technology (ASIANCON) · doi:10.1109/asiancon62057.2024.10837960 · Cited by 2 · see citing works

CNN-Based Detection of Cracks and Moulds in Buildings

Maheysh, V., & Kirthica, S. (2023) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-99-0835-6_52 · Cited by 2 · see citing works

Multidimensional discriminant analysis of species, strains and culture age of closely related entomopathogenic fungi using reflectance spectroscopy

Jasiewicz, J., Piekarczyk, J., Stępień, Ł., Tkaczuk, C., Sosnowska, D., Urbaniak, M., & Ratajkiewicz, H. (2024) · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2024.124135 · Cited by 2 · see citing works

DEVELOPMENT OF AN AUTOMATED DEBRIS DETECTION SYSTEM FOR WILD BLUEBERRY HARVESTERS USING A CONVOLUTIONAL NEURAL NETWORK TO IMPROVE FRUIT QUALITY

Das, A. (2020) · DalSpace (Dalhousie University) · Cited by 2 · see citing works

Microscopic Imaging and Labeling Dataset for the Detection of Pneumocystis jirovecii Using Methenamine Silver Staining Method

Reyes-Vera, E., Botero-Valencia, J., Arango, K., Zuluaga, A., & Naranjo, T. W. (2022) · Data · doi:10.3390/data7050056 · Cited by 2 · see citing works

Classifying bacteria clones using attention-based deep multiple instance learning interpreted by persistence homologyPreprint

Borowa, A., Rymarczyk, D., Ochońska, D., Brzychczy‐Włoch, M., & Zieliński, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.01189 · Cited by 2 · see citing works

Nanotechnology for rhizosphere engineering

Laraib, I., Ahmad, M. S. A., Rehman, A., & Imran, A. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-89973-4.00001-6 · Cited by 1 · see citing works

Bacterial perception-enhanced detection transformer in time-lapse images

Liang, Y., Zheng, L., Li, Z., Meng, S., Song, Y., Xu, Y., & Wang, L. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.109160 · Cited by 1 · see citing works

Explainable deep learning techniques for microscopic fungi classification using a learnable threshold-based ReLULeaky activation function and transfer learning

Hossain, M., Islam, M. K., Uddin, M., Jahan, F., Chowdhury, M. S. A., & Ahadullah, M. (2026) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2026.100683 · Cited by 1 · see citing works

Swin-AFR-FPN: Attention and fusion refined object detection model with swin-transformer backbone for Escherichia coli (E. coli) bacterial cell cycle identification and antibiotic phenotyping

Baydi̇lli̇, Y. Y. (2026) · The Journal of Supercomputing · doi:10.1007/s11227-026-08244-8 · Cited by 1 · see citing works

Artificial Intelligence in Microbiology

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Image-based approach for fungal network analysis: reconstructing connectivity with occluded information

Sten, O., Dottore, E. D., Pugno, N. M., & Mazzolai, B. (2023) · IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) · doi:10.1109/metroagrifor58484.2023.10424328 · Cited by 1 · see citing works

Classification of Pathogenic Molds on Peanuts using Image Processing and CNN

Indino, M. S., Rosario, A. D. C., & Villaverde, J. F. (2024) · IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) · doi:10.1109/iicaiet62352.2024.10730418 · Cited by 1 · see citing works

Sporify: An Automated Tool to Quantify Spores in Z-Stacked 3D Samples

Sten, O., Dottore, E. D., Raffaele, G., Ronzan, M., Pugno, N. M., & Mazzolai, B. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-39504-8_12 · Cited by 1 · see citing works

Microscopic image dataset of fungal spores for computer vision applications in Tectona grandis and other taxa

Islam, S. M. R., Sazzad, M. S. H., Ahamed, M. F., Ahmed, M., Nayon, S. I., Islam, T., Ayon, S. S., Haider, J., Abdullah-Al-Wadud, M., & Islam, S. M. R. (2025) · Scientific Data · doi:10.1038/s41597-025-06264-2 · Cited by 1 · see citing works

Aspergillus Niger Fungus Detection Using Deep Convolutional Neural Network with Principal Component Analysis and Chebyshev Neural Network

Vanitha, V., & Sornam, M. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-9012-9_19

Derin Öğrenme ile Aspergillus Mantar Türlerinin Sınıflandırılması

SAFARLI, S., Gürel, U., & Öz, Y. (2023) · Eskişehir Türk Dünyası Uygulama ve Araştırma Merkezi Bilişim Dergisi · doi:10.53608/estudambilisim.1296238

Ai-Assisted Classification of Microorganism Strains on Paper-Based Cultural RelicsPreprint

Liu, C., Ben, S., Liu, C., Li, X., Meng, Q., & Hao, Y. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4573024

Fungus Classification in Peanuts from Smart Lens Imagery Using Convolutional Neural Network

Dittakan, K., Thaenthong, J., Rodkuen, S., & Thungklang, P. (2025) · Current Applied Science and Technology · doi:10.55003/cast.2025.265905

Dynamic Multi-Objective None-Sorted Genetic Algorithm III Deep Long Short-Term Memory Incorporating Dropout for Collision Detection in Internet of Vehicles

Abdullahi, S., Gital, A. Y., Zambuk, F. U., Mustapaha, S. W., & Abdullahi, A. U. (2025) · International Journal of Latest Technology in Engineering Management & Applied Science · doi:10.51583/ijltemas.2025.1410000059

CBAM-Enhanced EfficientNet for Interpretable Microscopic Fungal Classification

Riya, K. S., & Jose, C. J. (2025) · International Conference on Smart Computing and Communications (ICSCC) · doi:10.1109/icscc66177.2025.11233764

TLFungID: Transfer Learning for Invasive Fungal Aspergillus spp. - Identification

Khattiyawech, C., Prempraneerach, P., Samosornsuk, W., Hematulin, P., Boonmee, P., & Samosornsuk, S. (2025) · doi:10.1109/icitee66631.2025.11338409

Artificial Intelligence and Machine Learning for Microbial Deciphering

Sobti, R. C., & Khan, M. A. (2026) · Springer eBooks · doi:10.1007/978-981-95-8840-4_8

Species classification and hyphae segmentation of soilborne fungi and oomycetes using microfluidic soil chips and deep learning image analysisPreprint

Karlsson, E., Forsbacka, J., Sopasakis, A., Ohlsson, P., Hammer, E. C., & Zou, H. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7049403

Automated Classification and Quantification of Fungal Spores in Aerobiological Sampling Using Deep LearningPreprint

Jiménez-Uribe, D. A., Sánchez, D. G., Jimenez, J. D., Barrios, R. L. A., Labrador, C. R., Altamar-Mercado, H., & Patiño-Vanegas, A. (2026) · Research Square · doi:10.21203/rs.3.rs-9997567/v1

Deciphering fungal-microbial interactions: From microscale experimental models to integrative systems biology

Li, X., & Haslinger, K. (2026) · Biotechnology Advances · doi:10.1016/j.biotechadv.2026.109066

Comparative analysis and optimization of multi-model artificial intelligence for microscopic detection of fungal structures in superficial fungal infections

Liu, M., He, W., Ni, L., Li, L., Tang, Y., Tan, J., & Yang, L. (2026) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2026.1892808

Pilzerkennung mit Bildverarbeitung und maschinellem Lernen

Tahir, M. W. (2019) · Media (https://www.suub.uni-bremen.de/)

Fungus Detection Using Computer Vision and Machine Learning Techniques

Tahir, M. W. (2019) · Cited by 1 · see citing works

Exploiting Convolutional Neural Network for Automatic Fungus Detection in Microscope Images

Prommakhot, A., & Srinonchat, J. (2020) · International Electrical Engineering Congress (iEECON) · doi:10.1109/ieecon48109.2020.229532 · Cited by 9 · see citing works

Fungus Detection and Identification using Computer Vision Techniques and Convolution Neural Networks

Nair, A., Manomohan, N. K., Reddy, H., Pandey, S., & Kadam, P. (2021) · International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON) · doi:10.1109/smartgencon51891.2021.9645827 · Cited by 6 · see citing works

Innovative Fungal Disease Diagnosis System Using Convolutional Neural燦etwork

Alyas, T., Alissa, K. A., Mohammad, A. S., Asif, S., Faiz, T., & Ahmed, G. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.031376 · Cited by 5 · see citing works