“How many images do I need?” Understanding how sample size per class affects deep learning model performance metrics for balanced designs in autonomous wildlife monitoring

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333

Densely Connected Convolutional Networks

Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566

Distilling the Knowledge in a Neural Network

Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204

Generalized Additive Models: An Introduction with R

Wood, S. N. (2017) · doi:10.1201/9781315370279 · Cited by 13,852

Advances in Neural Information Processing Systems 25

Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653

Generalized Additive Models

Hastie, T., & Tibshirani, R. (1986) · Statistical Science · doi:10.1214/ss/1177013604 · Cited by 12,575

Generalized Additive Models: An Introduction With R

Chiang, A. Y. (2007) · Technometrics · doi:10.1198/tech.2007.s505 · Cited by 8,532

Generalized Additive Models.

Brown, R. A., Hastie, T. J., & Tibshirani, R. J. (1991) · Biometrics · doi:10.2307/2532174 · Cited by 8,263

Detection of Cardiovascular Diseases in ECG Images Using Machine Learning and Deep Learning Methods

Abubaker, M., & Babayiğit, B. (2022) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2022.3159505 · Cited by 243

Deep learning for survival analysis: a review

Wiegrebe, S., Kopper, P., Sonabend, R., Bischl, B., & Bender, A. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-023-10681-3 · Cited by 226

Artificial intelligence for carbon emissions using system of systems theory

Gaur, L., Afaq, A., Arora, G. K., & Khan, N. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102165 · Cited by 187

The future of General Movement Assessment: The role of computer vision and machine learning – A scoping review

Silva, N., Zhang, D., Kulvičius, T., Gail, A., Barreiros, C., Lindstaedt, S., Kraft, M., Bölte, S., Poustka, L., Nielsen‐Saines, K., et al. (2021) · Research in Developmental Disabilities · doi:10.1016/j.ridd.2021.103854 · Cited by 137

Self-supervised deep learning model for COVID-19 lung CT image segmentation highlighting putative causal relationship among age, underlying disease and COVID-19

Fung, D. L. X., Liu, Q., Zammit, J., Leung, C. K.-S., & Hu, P. (2021) · Journal of Translational Medicine · doi:10.1186/s12967-021-02992-2 · Cited by 104

Defect detection of the surface of wind turbine blades combining attention mechanism

Liu, Y., Zheng, Y., Shao, Z., Wei, T., Cui, T., & Xu, R. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.102292 · Cited by 81

Automated Muzzle Detection and Biometric Identification via Few-Shot Deep Transfer Learning of Mixed Breed Cattle

Shojaeipour, A., Falzon, G., Kwan, P., Hadavi, N., Cowley, F. C., & Paúl, D. (2021) · Agronomy · doi:10.3390/agronomy11112365 · Cited by 84

Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection

Aust, J., Shankland, S., Pons, D., Mukundan, R., & Mitrovic, A. (2021) · Aerospace · doi:10.3390/aerospace8020030 · Cited by 66

Review of applications of deep learning in veterinary diagnostics and animal health

Xiao, S., Dhand, N. K., Wang, Z., Hu, K., Thomson, P. C., House, J. K., & Khatkar, M. S. (2025) · Frontiers in Veterinary Science · doi:10.3389/fvets.2025.1511522 · Cited by 86

Generative Adversarial Networks (GANs) for Image Augmentation in Farming: A Review

Rahman, Z. U., Asaari, M. S. M., Ibrahim, H., Abidin, I. S. Z., & Ishak, M. K. (2024) · IEEE Access · doi:10.1109/access.2024.3505989 · Cited by 71

Heuristic hyperparameter optimization of deep learning models for genomic prediction

Han, J., Gondro, C., Reid, K. N., & Steibel, J. P. (2021) · G3 Genes Genomes Genetics · doi:10.1093/g3journal/jkab032 · Cited by 62

Deep Learning applied to Road Accident Detection with Transfer Learning and Synthetic Images

Tamagusko, T., Correia, M. G., Huynh, M. A., & Ferreira, A. (2022) · Transportation research procedia · doi:10.1016/j.trpro.2022.09.012 · Cited by 57

DeepWILD: Wildlife Identification, Localisation and estimation on camera trap videos using Deep learning

Simões, F., Bouveyron, C., & Precioso, F. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102095 · Cited by 54

Iterative human and automated identification of wildlife images

Miao, Z., Liu, Z., Gaynor, K. M., Palmer, M. S., Yu, S. X., & Getz, W. M. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00393-0 · Cited by 61

Corn leaf disease: insightful diagnosis using VGG16 empowered by explainable AI

Tariq, M., Ali, U., Abbas, S., Hassan, S. G., Naqvi, R. A., Khan, M. A., & Jeong, D. (2024) · Frontiers in Plant Science · doi:10.3389/fpls.2024.1402835 · Cited by 61

Computed tomography and radiation dose images-based deep-learning model for predicting radiation pneumonitis in lung cancer patients after radiation therapy

Zhang, Z., Wang, Z., Luo, T., Yan, M., Dekker, A. L. A. J., Ruysscher, D. D., Traverso, A., Wee, L., & Zhao, L. (2023) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2023.109581 · Cited by 46

Synthetic Data for Deep Learning in Computer Vision & Medical Imaging: A Means to Reduce Data Bias

Paproki, A., Salvado, O., & Fookes, C. (2024) · ACM Computing Surveys · doi:10.1145/3663759 · Cited by 57

Detecting wildlife trafficking in images from online platforms: A test case using deep learning with pangolin images

Cardoso, A. S., Bryukhova, S., Renna, F., Reino, L., Xu, C., Xiao, Z., Correia, R. A., Minin, E. D., Ribeiro, J., & Vaz, A. S. (2023) · Biological Conservation · doi:10.1016/j.biocon.2023.109905 · Cited by 47

Current state and prospects of artificial intelligence in allergy

van Breugel, M., Fehrmann, R. S. N., Bügel, M., Rezwan, F. I., Holloway, J. W., Nawijn, M. C., Fontanella, S., Čustović, A., & Koppelman, G. H. (2023) · Allergy · doi:10.1111/all.15849 · Cited by 46

An Intelligence Cattle Reidentification System Over Transport by Siamese Neural Networks and YOLO

Bakhshayeshi, I., Erfani, E., Taghikhah, F. R., Elbourn, S., Beheshti, A., & Asadnia, M. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3294944 · Cited by 43

Deep learning-based activity-aware 3D human motion trajectory prediction in construction

Heravi, M. Y., Jang, Y., Jeong, I., & Sarkar, S. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122423 · Cited by 44

Multi-classification deep neural networks for identification of fish species using camera captured images

Malik, H., Naeem, A., Hassan, S. G., Ali, F., Naqvi, R. A., & Yon, D. K. (2023) · PLoS ONE · doi:10.1371/journal.pone.0284992 · Cited by 47

A deep learning approach for Maize Lethal Necrosis and Maize Streak Virus disease detection

O’Halloran, T., Obaido, G., Otegbade, B., & Mienye, I. D. (2024) · Machine Learning with Applications · doi:10.1016/j.mlwa.2024.100556 · Cited by 44

A workflow for segmenting soil and plant X-ray computed tomography images with deep learning in Google’s Colaboratory

Rippner, D. A., Raja, P. V., Earles, J. M., Momayyezi, M., Buchko, A. C., Duong, F. V., Forrestel, E. J., Parkinson, D. Y., Shackel, K. A., Neyhart, J., et al. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2022.893140 · Cited by 36

Construction worker productivity evaluation using action recognition for foreign labor training and education: A case study of Taiwan

Cheng, M., Khitam, A. F. K., & Tanto, H. H. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.104809 · Cited by 35

Automated location invariant animal detection in camera trap images using publicly available data sources

Shepley, A., Falzon, G., Meek, P. D., & Kwan, P. (2021) · Ecology and Evolution · doi:10.1002/ece3.7344 · Cited by 35

Using Web images to train a deep neural network to detect sparsely distributed wildlife in large volumes of remotely sensed imagery: A case study of polar bears on sea ice

Chabot, D., Stapleton, S., & Francis, C. M. (2022) · Ecological Informatics · doi:10.1016/j.ecoinf.2021.101547 · Cited by 32

Deep Learning Based on ResNet-18 for Classification of Prostate Imaging-Reporting and Data System Category 3 Lesions

Kang, Z., Xiao, E., Li, Z., & Wang, L. (2024) · Academic Radiology · doi:10.1016/j.acra.2023.12.042 · Cited by 32

Cross-institutional outcome prediction for head and neck cancer patients using self-attention neural networks

Le, W., Vorontsov, E., Romero, F. P., Seddik, L., Elsharief, M. M., Nguyen‐Tan, P. F., Roberge, D., Bahig, H., & Kadoury, S. (2022) · Scientific Reports · doi:10.1038/s41598-022-07034-5 · Cited by 30

Quality prediction of seabream Sparus aurata by deep learning algorithms and explainable artificial intelligence

GENÇ, İ. Y., Gürfidan, R., & Yi̇ği̇t, T. (2025) · Food Chemistry · doi:10.1016/j.foodchem.2025.143150 · Cited by 29

Traffic congestion-aware graph-based vehicle rerouting framework from aerial imagery

Bayraktar, E., Korkmaz, B. N., Erarslan, A. U., & Çelebi, N. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105769 · Cited by 29

Visual classification of pressure injury stages for nurses: A deep learning model applying modern convolutional neural networks

Seo, S., Kang, J., Eom, I. H., Song, H., Park, J. H., Lee, Y., & Lee, H. (2023) · Journal of Advanced Nursing · doi:10.1111/jan.15584 · Cited by 29

Wildlife detection, counting and survey using satellite imagery: are we there yet?

Delplanque, A., Théau, J., Foucher, S., Serati, G., Durand, S., & Lejeune, P. (2024) · GIScience & Remote Sensing · doi:10.1080/15481603.2024.2348863 · Cited by 34

Object classification and visualization with edge artificial intelligence for a customized camera trap platform

Nazir, S., & Kaleem, M. (2024) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102453 · Cited by 26

Mapping the Delivery of Ecological Functions Combining Field Collected Data and Unmanned Aerial Vehicles (UAVs)

Schenone, S., Azhar, M., Ramírez, C. A. V., Gastelum‐Strozzi, A., Delmas, P. J., & Thrush, S. F. (2021) · Ecosystems · doi:10.1007/s10021-021-00694-w · Cited by 24

Using artificial intelligence to find design errors in the engineering drawings

Джусупова, Р., Banotra, R., Bosch, J., & Olsson, H. H. (2023) · Journal of Software Evolution and Process · doi:10.1002/smr.2543 · Cited by 24

Sustainable land use scenarios generated by optimizing ecosystem distribution based on temporal and spatial patterns of ecosystem services in the southern China hilly region

Shao, Y., Xiao, Y., Kou, X., & Sang, W. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102275 · Cited by 24

GeoCrack: A High-Resolution Dataset For Segmentation of Fracture Edges in Geological Outcrops

Yaqoob, M., Ishaq, M., Ansari, M. Y., Konagandla, V. R. S., Tamimi, T. A., Tavani, S., Corradetti, A., & Seers, T. (2024) · Scientific Data · doi:10.1038/s41597-024-04107-0 · Cited by 22

Rapid detection of microfibres in environmental samples using open-source visual recognition models

Galata, S., Walkington, I., Lane, T., Kiriakoulakis, K., & Dick, J. (2024) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2024.135956 · Cited by 20

A novel feature descriptor based coral image classification using extreme learning machine with ameliorated chimp optimization algorithm

Ganesan, A., & Santhanam, S. M. (2021) · Ecological Informatics · doi:10.1016/j.ecoinf.2021.101527 · Cited by 21

Filipino Sign Language Recognition using Deep Learning

Montefalcon, M. D., Padilla, J. R., & Rodriguez, R. L. (2021) · International Conference on E-Society, E-Education and E-Technology (ICSET) · doi:10.1145/3485768.3485783 · Cited by 21

A semi-automatic workflow to process images from small mammal camera traps

Böhner, H., Kleiven, E. F., Ims, R. A., & Soininen, E. M. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102150 · Cited by 21

A few-shot rare wildlife image classification method based on style migration data augmentation

Zhang, Q., Yi, X., Guo, J., Tang, Y., Feng, T., & Liu, R. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102237 · Cited by 23

An Efficient Method for Detecting Asphalt Pavement Cracks and Sealed Cracks Based on a Deep Data-Driven Model

Yang, N., Li, Y., & Ma, R. (2022) · Applied Sciences · doi:10.3390/app121910089 · Cited by 21

Augmented Decision-Making in wound Care: Evaluating the clinical utility of a Deep-Learning model for pressure injury staging

Kim, J., Lee, C., Choi, S.-C., Sung, D.-I., Seo, J., Lee, Y. N., Lee, J. H., Han, E. J., Kim, A. Y., Park, H. S., et al. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2023.105266 · Cited by 19

A Comprehensive Survey of Animal Identification: Exploring Data Sources, AI Advances, Classification Obstacles and the Role of Taxonomy

Zhang, Q., Ahmed, K. E. U., Sharda, N. K., & Wang, H. (2024) · International Journal of Intelligent Systems · doi:10.1155/2024/7033535 · Cited by 24

Improving the Accuracy of Species Identification by Combining Deep Learning With Field Occurrence Records

Sun, J., Futahashi, R., & Yamanaka, T. (2021) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2021.762173 · Cited by 18

Quality Determination of Frozen-Thawed Shrimp Using Machine Learning Algorithms Powered by Explainable Artificial Intelligence

GENÇ, İ. Y., Gürfidan, R., & Açıkgözoğlu, E. (2025) · Food Analytical Methods · doi:10.1007/s12161-025-02768-0 · Cited by 17

Detection and prediction of foam evolution during the bottling of noncarbonated beverages using artificial neural networks

Morelle, E., Rudolph, A., McHardy, C., & Rauh, C. (2021) · Food and Bioproducts Processing · doi:10.1016/j.fbp.2021.03.017 · Cited by 17

Identifying Habitat Elements from Bird Images Using Deep Convolutional Neural Networks

Wang, Z., Wang, J., Lin, C., Yan, H., Wang, Z., & Ji, L. (2021) · Animals · doi:10.3390/ani11051263 · Cited by 16

Assessment of the resilience of pedestrian roads based on image deep learning models

Ku, D., Choi, M., Oh, H., Shin, S., & Lee, S. (2022) · Proceedings of the Institution of Civil Engineers - Municipal Engineer · doi:10.1680/jmuen.21.00037 · Cited by 15

Pattern Recognition Method for Detecting Engineering Errors on Technical Drawings

Джусупова, Р., Banotra, R., Bosch, J., & Olsson, H. H. (2022) · IEEE World AI IoT Congress (AIIoT) · doi:10.1109/aiiot54504.2022.9817294 · Cited by 15

Convolutional Neural Network Models for Visual Classification of Pressure Ulcer Stages: Cross-Sectional Study

Lei, C., Jiang, Y., Xu, K., Liu, S., Cao, H., & Wang, C. (2025) · JMIR Medical Informatics · doi:10.2196/62774 · Cited by 14

Expanding NEON biodiversity surveys with new instrumentation and machine learning approaches

Kitzes, J. A., Blake, R. E., Bombaci, S., Chapman, M., Durán, S. M., Huang, T., Joseph, M. B., Lapp, S., Marconi, S., Oestreich, W. K., et al. (2021) · Ecosphere · doi:10.1002/ecs2.3795 · Cited by 14

Occluded object detection and exposure in cluttered environments with automated hyperspectral anomaly detection

Hanson, N., Lvov, G., & Padır, T. (2022) · Frontiers in Robotics and AI · doi:10.3389/frobt.2022.982131 · Cited by 14

Fit-For-Purpose Creativity Assessment: Automatic Scoring of the Test of Creative Thinking – Drawing Production (TCT-DP)

Cropley, D. H., Theurer, C., Mathijssen, A. C. S., & Marrone, R. L. (2024) · Creativity Research Journal · doi:10.1080/10400419.2024.2339667 · Cited by 18

Applying Knowledge-Based and Data-Driven Methods to Improve Ore Grade Control of Blast Hole Drill Cuttings Using Hyperspectral Imaging

Akbar, S., Abdolmaleki, M., Ghadernejad, S., & Esmaeili, K. (2024) · Remote Sensing · doi:10.3390/rs16152823 · Cited by 13

Desert bighorn sheep (Ovis canadensis) recognition from camera traps based on learned features

Vargas-Felipe, M., Pellegrin, L., Guevara-Carrizales, A. A., López-Monroy, A. P., Escalante, H. J., & Gonzalez-Fraga, J. A. (2021) · Ecological Informatics · doi:10.1016/j.ecoinf.2021.101328 · Cited by 13

Performance Evaluation of Different Object Detection Models for the Segmentation of Optical Cups and Discs

Alfonso-Francia, G., Pedraza‐Ortega, J. C., Badillo, M., Toledano‐Ayala, M., Aceves-Fernández, M. A., Rodríguez‐Reséndiz, J., Ko, S., & Tovar‐Arriaga, S. (2022) · Diagnostics · doi:10.3390/diagnostics12123031 · Cited by 16

Animals and climate change: A visual and discourse network analysis of Instagram posts

Koop‐Monteiro, Y., Stoddart, M. C. J., & Tindall, D. B. (2023) · Environmental Sociology · doi:10.1080/23251042.2023.2216371 · Cited by 16

Hydraulic fracturing performance analysis by the mutual information and Gaussian process regression methods

Xu, Y., Liu, D., Xu, W., He, D., & Hao, H. (2023) · Engineering Fracture Mechanics · doi:10.1016/j.engfracmech.2023.109285 · Cited by 11

An Assessment of Human Inspection and Deep Learning for Defect Identification in Floral Wreaths

Caballero-Ramírez, D., Baez-Lopez, Y. A., Limón-Romero, J., Tortorella, G. L., & Tlapa, D. (2023) · Horticulturae · doi:10.3390/horticulturae9111213 · Cited by 10

Machine learning-based wood anatomy identification: towards anatomical feature recognition

He, X., Pelt, D. M., Gao, J., Gravendeel, B., Zhu, P., Chen, S., Qiu, J., & Lens, F. (2024) · IAWA Journal - KU Leuven/IAWA Journal · doi:10.1163/22941932-bja10157 · Cited by 16

Acoustic estimation of the manatee population and classification of call categories using artificial intelligence

Schneider, S., von Fersen, L., & Dierkes, P. W. (2024) · Frontiers in Conservation Science · doi:10.3389/fcosc.2024.1405243 · Cited by 10

Monitoring of Electrophysiological Functions in Brain‐on‐a‐Chip and Brain Organoids

Song, J., Jeong, H. E., Choi, A. H., & Kim, H. N. (2024) · Advanced NanoBiomed Research · doi:10.1002/anbr.202400052 · Cited by 11

Effect of training sample size, image resolution and epochs on filamentous and floc-forming bacteria classification using machine learning

Alani, S., Guo, H., Fyfe, S., Long, Z., Donnaz, S., & Kim, Y. (2025) · Journal of Environmental Management · doi:10.1016/j.jenvman.2025.124803 · Cited by 10

Measuring inter-individual differences in behavioural types of gilthead seabreams in the laboratory using deep learning

Signaroli, M., Lana, A., Martorell‐Barceló, M., Sanllehi, J., Barcelo‐Serra, M., Aspillaga, E., Mulet, J., & Alós, J. (2022) · PeerJ · doi:10.7717/peerj.13396 · Cited by 10

A Study on Sample Size Sensitivity of Factory Manufacturing Dataset for CNN-Based Defective Product Classification

Kim, D., Seo, S. B., Yoo, N., & Shin, G. (2022) · Computation · doi:10.3390/computation10080142 · Cited by 10

A novel application of deep learning to forensic hair analysis methodology

Airlie, M., Robertson, J. A., Ma, W., Airlie, D., & Brooks, E. (2022) · Australian Journal of Forensic Sciences · doi:10.1080/00450618.2022.2159064 · Cited by 9

LarvaeCountAI: a robust convolutional neural network-based tool for accurately counting the larvae of Culex annulirostris mosquitoes

Javed, N., López‐Denman, A. J., Paradkar, P. N., & Bhatti, A. (2024) · Acta Tropica · doi:10.1016/j.actatropica.2024.107468 · Cited by 9

An automatic identification method of common species based on ensemble learning

Li, H., Zhang, M., Meng, D.-Y., Bo, G., Li, Z., Huang, C.-F., Li, W.-K., Jiang, H.-L., Wu, R., Li, X., et al. (2025) · Ecological Informatics · doi:10.1016/j.ecoinf.2025.103046 · Cited by 10

On herbarium specimen images and artificial intelligence

Tessler, M., & Little, D. P. (2025) · New Phytologist · doi:10.1111/nph.70312 · Cited by 13

Impact of the Volume and Distribution of Training Datasets in the Development of Deep-Learning Models for the Diagnosis of Colorectal Polyps in Endoscopy Images

Gong, E. J., Bang, C. S., Lee, J. J., Yang, Y. J., & Baik, G. H. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12091361 · Cited by 9

An ingenious deep learning approach for pressure injury depth evaluation with limited data

Ikuta, K., Fukuoka, K., Kimura, Y., Nakagaki, M., Ohga, M., Suyama, Y., Morita, M., Umeda, R., Konishi, M., Nishikawa, H., et al. (2024) · Journal of Tissue Viability · doi:10.1016/j.jtv.2024.05.009 · Cited by 9

Fusion of colour contrasted images for early detection of oesophageal squamous cell dysplasia from endoscopic videos in real time

Gao, X. W., Taylor, S., Pang, W., Hui, R., Lü, X., & Braden, B. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.11.023 · Cited by 9

Human Supervision is Key to Achieving Accurate AI-assisted Wildlife Identifications in Camera Trap Images

Huebner, S. E., Palmer, M. S., & Packer, C. (2024) · Citizen Science Theory and Practice · doi:10.5334/cstp.752 · Cited by 8

Predicting hepatocellular carcinoma response to TACE: A machine learning study based on 2.5D CT imaging and deep features analysis

Chong, L., Cao, T., Tang, M., Pu, W., & Lei, P. (2025) · European Journal of Radiology · doi:10.1016/j.ejrad.2025.112060 · Cited by 9

Evaluation methods of pressure injury stages: A systematic review and meta-analysis

Chao, Q., Pei, J., Wei, Y., Yang, Z., Wang, X., Li, D., & Han, L. (2025) · Journal of Tissue Viability · doi:10.1016/j.jtv.2025.100894 · Cited by 8

Platelets Image Classification Through Data Augmentation: A Comparative Study of Traditional Imaging Augmentation and GAN-Based Synthetic Data Generation Techniques Using CNNs

Abidoye, I., Ikeji, F., Coupland, C. A., Calaminus, S. D. J., Sander, N., & de Sousa, E. M. V. (2025) · Journal of Imaging · doi:10.3390/jimaging11060183 · Cited by 8

Dataset Transformation System for Sign Language Recognition Based on Image Classification Network

Choi, S.-G., Park, Y., & Sohn, C.-B. (2022) · Applied Sciences · doi:10.3390/app121910075 · Cited by 8

Classification of Kidney Tumor Grading on Preoperative Computed Tomography Scans

Mahootiha, M., Qadir, H. A., Bergsland, J., & Balasingham, I. (2023) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-34586-9_6 · Cited by 8

Semantic segmentation framework for atoll satellite imagery: An in-depth exploration using UNet variants and Segmentation Gym

Wang, R., Chowdhury, T., & Ortiz, A. C. (2024) · Applied Computing and Geosciences · doi:10.1016/j.acags.2024.100217 · Cited by 7

IoT Enabled Smart Agriculture System for Detection and Classification of Tomato and Brinjal Plant Leaves Disease

Kasera, R. K., Nath, S., Das, B., Kumar, A., & Acharjee, T. (2025) · Scalable Computing Practice and Experience · doi:10.12694/scpe.v26i1.3826 · Cited by 7

Unveiling the impact of dataset size on machine learning models for anxiety and depression prediction amid the COVID-19 pandemic: determining optimal data collection thresholds

Arora, P., & Dahiya, S. (2025) · Current Psychology · doi:10.1007/s12144-025-07432-8 · Cited by 7

A Novel Partitioned Random Forest Method-Based Facial Emotion Recognition

Heidari, H., Murugappan, M., Mohammed, J. S., & Chowdhury, M. E. H. (2025) · IEEE Access · doi:10.1109/access.2025.3560362 · Cited by 7

Annotation Quality Versus Quantity for Object Detection and Instance Segmentation

Agnew, C., Scanlan, A., Denny, P., Grua, E. M., van de Ven, P. W. J., & Eising, C. (2024) · IEEE Access · doi:10.1109/access.2024.3467008 · Cited by 10

Custom Based Obstacle Detection Using Yolo v3 for Low Flying Drones

Aswini, N., & Uma, S., V (2021) · International Conference on Circuits, Controls and Communications (CCUBE) · doi:10.1109/ccube53681.2021.9702739 · Cited by 7

A Novel Approach to the Authentication of Apricot Seed Cultivars Using Innovative Models Based on Image Texture Parameters

Ropelewska, E., Sabancı, K., Aslan, M. F., & Azizi, A. (2022) · Horticulturae · doi:10.3390/horticulturae8050431 · Cited by 7

Testing the detection of large, secretive snakes using camera traps

Walkup, D. K., Ryberg, W. A., Pierce, J. B., Smith, E., Childress, J., East, F., Pierce, B. L., Brown, P., Fielder, C. M., & Hibbitts, T. J. (2022) · Wildlife Society Bulletin · doi:10.1002/wsb.1408 · Cited by 7

Sampling biases across interaction types affect the robustness of ecological multilayer networks

Hervías‐Parejo, S., Traveset, A., Nogales, M., Heleno, R., Llewelyn, J., & Strona, G. (2025) · Ecological Informatics · doi:10.1016/j.ecoinf.2025.103183 · Cited by 6

Weed detection in cabbage fields using RGB and NIR images

Hruška, A., Hamouz, P., Lev, J., Pavlíček, J., Kroulík, M., Hamouzová, K., Košnarová, P., Holec, J., & Kouřím, P. (2025) · Smart Agricultural Technology · doi:10.1016/j.atech.2025.101232 · Cited by 6

A New Framework Containing Convolution and Pooling Circuits for Image Processing and Deep Learning Applications with Quantum Computing Implementation

Yetış, H., & Karaköse, M. (2022) · Traitement du signal · doi:10.18280/ts.390212 · Cited by 6

Monitoring tropical freshwater fish with underwater videography and deep learning

Jansen, A., van Bodegraven, S., Esparon, A. J., Gadhiraju, V., Walker, S., Buccella, C., Bock, K., Loewensteiner, D. A., Mooney, T. J., Harford, A. J., et al. (2024) · Marine and Freshwater Research · doi:10.1071/mf23166 · Cited by 6

Early and non-destructive prediction of the differentiation efficiency of human induced pluripotent stem cells using imaging and machine learning

Hojo, M. A., Tsuzuki, T., Ozawa, Y., Araki, T., & Sakurai, H. (2025) · Scientific Reports · doi:10.1038/s41598-025-11108-5 · Cited by 6

Detection and Classification of Human-Carrying Baggage Using DenseNet-161 and Fit One Cycle

Ramadan, M. K., Youssif, A. A. A., & El-Behaidy, W. H. (2022) · Big Data and Cognitive Computing · doi:10.3390/bdcc6040108 · Cited by 7

Instance segmentation of oyster mushroom datasets: A novel data sampling methodology for training and evaluation of deep learning models

Charisis, C., Wang, M., & Argyropoulos, D. (2025) · Smart Agricultural Technology · doi:10.1016/j.atech.2025.101146 · Cited by 5

Copy‐Paste Augmentation Improves Automatic Species Identification in Camera Trap Images

Mesnage, C. S., Corbett, A., Curry, J., Rowlands, S., Dutta, A., Everson, R., & Simmons, B. I. (2025) · Ecology and Evolution · doi:10.1002/ece3.72357 · Cited by 6

APD-YOLOv7: Enhancing Sustainable Farming through Precise Identification of Agricultural Pests and Diseases Using a Novel Diagonal Difference Ratio IOU Loss

Li, J., Liu, S., Chen, D., Zhou, S., & Li, C. (2024) · Sustainability · doi:10.3390/su16208855 · Cited by 5

Trans-m5C: A transformer-based model for predicting 5-methylcytosine (m5C) sites

Fu, H., Ding, Z., & Wang, W. (2024) · Methods · doi:10.1016/j.ymeth.2024.12.010 · Cited by 5

Sounds of the deep: How input representation, model choice, and dataset size influence underwater sound classification performance

Olcay, A., White, P. R., Bull, J. M., Risch, D., Dell, B. L., & White, E. L. (2025) · The Journal of the Acoustical Society of America · doi:10.1121/10.0036498 · Cited by 5

Deep learning‐based ecological analysis of camera trap images is impacted by training data quality and quantity

Bevan, P. A., Pantazis, O., Pringle, H. A. I., Ferreira, G. B., Ingram, D. J., Madsen, E. K., Thomas, L., Thanet, D. R., Silwal, T., Rayamajhi, S., et al. (2026) · Remote Sensing in Ecology and Conservation · doi:10.1002/rse2.70052 · Cited by 19

Automated single species identification in camera trap images: architecture choice, training strategies, and the interpretation of performance metricsPreprint

Burkard, Y., Francazi, E., Lavender, E., Dubach, T., Wehrli, S., Brodesen, J., Volpi, M., Baity‐Jesi, M., & Moor, H. (2024) · doi:10.32942/x2vw4v

Three critical factors affecting automated image species recognition performance for camera traps

Schneider, S., Greenberg, S., Taylor, G. W., & Kremer, S. C. (2020) · Ecology and Evolution · doi:10.1002/ece3.6147 · Cited by 182

Automated wildlife image classification: An active learning tool for ecological applications

Bothmann, L., Wimmer, L., Charrakh, O., Weber, T. R., Edelhoff, H., Peters, W., Nguyen, H., Benjamin, C., & Menzel, A. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102231 · Cited by 55

The DeepFaune initiative: a collaborative effort towards the automatic identification of European fauna in camera trap images

Rigoudy, N., Dussert, G., Benyoub, A., Besnard, A. G., Birck, C., Boyer, J., Bollet, Y., Bunz, Y., de la Peña, G. C. G., Chetouane, E., et al. (2023) · European Journal of Wildlife Research · doi:10.1007/s10344-023-01742-7 · Cited by 53