Development of convolutional neural network and its application in image classification: a survey

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148

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

Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946

Deep learning

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

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812

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

Support-vector networks

Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421

Gradient-based learning applied to document recognition

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805

A Radar Signal Recognition Approach via IIF-Net Deep Learning Models

Li, J., Zhang, H., Ou, J., & Wang, W. (2020) · Computational Intelligence and Neuroscience · doi:10.1155/2020/8858588 · Cited by 9

Multi-Feature Broad Learning System for Image Classification

Liu, R., Liu, Y., Zhao, Y., Chen, X., Cui, S., Wang, F., & Yi, L. (2021) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001421500336 · Cited by 10

A Comparison of Time-Frequency Distributions for Deep Learning-Based Speech Assessment of Aphasic Patients

Kumar, A., Mahmoud, S. S., Wang, Y., Faisal, S., & Fang, Q. (2022) · Conference on Human System Interactions · doi:10.1109/hsi55341.2022.9869452 · Cited by 10

Comparing Approaches for Explaining DNN-Based Facial Expression Classifications

ter Burg, K., & Kaya, H. (2022) · Algorithms · doi:10.3390/a15100367 · Cited by 9

CONVOLUTIONAL NEURAL NETWORKS FOR DETECTING BRIDGE CROSSING EVENTS WITH GROUND-BASED INTERFEROMETRIC RADAR DATA

Arnold, M., Høyer, M., & Keller, S. (2021) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-v-1-2021-31-2021 · Cited by 9

A method for classifying tube structures based on shape descriptors and a random forest classifier

Huang, H., Liu, J., Liu, S., Wu, T., & Jin, P. (2020) · Measurement · doi:10.1016/j.measurement.2020.107705 · Cited by 9

Proposed Eye-Hand Correlation Assessment System: A Novel Approach for Evaluating Coordination

Copaci, D. S., Pernalete, N., Ortiz, Á. G., & Blanco, D. (2024) · IEEE Access · doi:10.1109/access.2023.3349300 · Cited by 8

Paddy Leaf Diseases Image Classification using Convolution Neural Network (CNN) Technique

Zainorzuli, S. M., Abdullah, S. A. C., Abidin, H. Z., & Ruslan, F. A. (2021) · IEEE Student Conference on Research and Development (SCOReD) · doi:10.1109/scored53546.2021.9652688 · Cited by 8

Testing ground-truth errors in an automotive dataset for a DNN-based object detector

Li, B., Baris, G., Chan, P. H., Rahman, A., & Donzella, V. (2022) · International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) · doi:10.1109/iceccme55909.2022.9988623 · Cited by 8

Classification and Detection of Cyanosis Images on Lightly and Darkly Pigmented Individual Human Skins using a Fine-Tuned MobileNet Architecture

Mpova, L., Shongwe, T., & Hasan, A. N. (2023) · International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD) · doi:10.1109/icabcd59051.2023.10220464 · Cited by 8

Privacy-Preserving Classification on Deep Learning with Exponential Mechanism

Ju, Q., Xia, R., Li, S., & Zhang, Z. (2024) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-024-00422-x · Cited by 8

Enhancing multimedia management: cloud-based movie type recognition with hybrid deep learning architecture

Lin, F., Yuan, J., Chen, Z., & Abiri, M. (2024) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-024-00668-y · Cited by 11

How to discriminate wood of CITES-listed tree species from their look-alikes: using an attention mechanism with the ResNet model on an enhanced macroscopic image dataset

Liu, S., Zheng, C., Wang, J., Lu, Y., Yao, J., Zou, Z., Yin, Y., & He, T. (2024) · Frontiers in Plant Science · doi:10.3389/fpls.2024.1368885 · Cited by 7

Progress and Thinking on Self-Supervised Learning Methods in Computer Vision: A Review

Chen, Z., Hu, B., Chen, Z., & Zhang, J. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3443885 · Cited by 11

Image processing and segmentation open source codes applied to FIB-SEM images of ultra-tight gas shales samples: Enhanced pore space representativeness and mineral identification

Lasnel, R., Frouté, L., Kovscek, A. R., Jolivet, I. C., & Creux, P. (2025) · Gas Science and Engineering · doi:10.1016/j.jgsce.2025.205610 · Cited by 7

Classification of fashion e-commerce products using ResNet-BERT multi-modal deep learning and transfer learning optimization

Seo, I.-J., Lee, Y.-H., & Jang, B. (2025) · PLoS ONE · doi:10.1371/journal.pone.0324621 · Cited by 8

Development of New Aerial Image Datasets and Deep Learning Methods for Waterfowl Detection and Classification

Zhang, Y., Wang, S., Zhai, Z., Shang, Y., Viegut, R., Webb, E. B., Raedeke, A. H., & Sartwell, J. (2022) · IEEE International Conference on Cognitive Machine Intelligence (CogMI) · doi:10.1109/cogmi56440.2022.00026 · Cited by 7

Deep Learning Models for Waterfowl Detection and Classification in Aerial Images

Zhang, Y., Feng, Y., Wang, S., Tang, Z., Zhai, Z., Viegut, R., Webb, E. B., Raedeke, A. H., & Shang, Y. (2024) · Information · doi:10.3390/info15030157 · Cited by 7

A deep learning framework for face verification without alignment

Fan, Z., & Guan, Y. (2020) · Journal of Real-Time Image Processing · doi:10.1007/s11554-020-01037-z · Cited by 7

An Automated Rapid Test for Viral Nanoparticles Based on Spatiotemporal Deep Learning

Wüstefeld, K., & Weichert, F. (2020) · Proceedings of IEEE Sensors/Proceedings of IEEE Sensors ... · doi:10.1109/sensors47125.2020.9278935 · Cited by 7

Gramian Angular Field and Convolutional Neural Networks for Real-Time Multiband Spectrum Sensing in Cognitive Radio Networks

Molina-Tenorio, Y., Prieto‐Guerrero, A., Rodríguez-Colina, E., Vásquez-Toledo, L. A., & Olvera-Guerrero, O. A. (2025) · Sensors · doi:10.3390/s25123580 · Cited by 6

Classification of Multiple H&E Images via an Ensemble Computational Scheme

Longo, L. C., Roberto, G. F., Tosta, T. A. A., de Faria, P. R., Loyola, A. M., Cardoso, S. V., Silva, A. B., do Nascimento, M. Z., & Neves, L. A. (2023) · Entropy · doi:10.3390/e26010034 · Cited by 6

Comprehensive Survey of State-of-the-Art Convolutional Neural Network Architectures and Their Applications in Image Classification

Pan, X., Luo, Z., & Zhou, L. (2022) · Innovations in Applied Engineering and Technology · doi:10.62836/iaet.v1i1.1006 · Cited by 7

A Semantic Segmentation Approach Based on DeepLab Network in High-Resolution Remote Sensing Images

Hu, H., Cai, S., Wang, W., Zhang, P., & Li, Z. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34113-8_25 · Cited by 6

Holographic memory updated by contradictory information: influence of low frequency attenuation on response stability

Pavlov, A. V. (2020) · Computer Optics · doi:10.18287/2412-6179-co-668 · Cited by 6

Modeling of a Generic Edge Computing Application Design

Roig, P. J., Alcaraz, S., Gilly, K., Bernad, C., & Juiz, C. (2021) · Sensors · doi:10.3390/s21217276 · Cited by 6

Classification of Partial Discharge Patterns in Rotating Electrical Machines Using Machine Learning

Lengsfeld, S., Rehwald, F., Ast, H., & Schroder, O. (2022) · International Conference on Electrical Machines (ICEM) · doi:10.1109/icem51905.2022.9910894 · Cited by 5

Deep Learning Approach for Hepatic Lesion Detection

Makram, M., & Mohammed, A. (2024) · Intelligent Methods, Systems, and Applications (IMSA) · doi:10.1109/imsa61967.2024.10652800 · Cited by 5

A Dual-Mode Approach for Vision-Based Navigation in a Lunar Landing Scenario

Ostrogovich, L., Prete, R. D., Tomasicchio, G., Longépé, N., & Renga, A. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00673 · Cited by 5

Toward Integrating ChatGPT Into Satellite Image Annotation Workflows: A Comparison of Label Quality and Costs of Human and Automated Annotators

Beck, J., Kemeter, L. M., Dürrbeck, K., Abdalla, M. H. I., & Kreuter, F. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3528192 · Cited by 8

A Discovery Strategy for Active Compounds of Chinese Medicine Based on the Prediction Model of Compound-Disease Relationship

Huo, M., Peng, S., Li, J., Zhang, Y., & Qiao, Y. (2022) · Journal of Oncology · doi:10.1155/2022/8704784 · Cited by 5

Detecting COVID-19 patients via MLES-Net deep learning models from X-Ray images

Wang, W., Jiang, Y., Wang, X., Zhang, P., & Li, J. (2022) · BMC Medical Imaging · doi:10.1186/s12880-022-00861-y · Cited by 5

Deep Learning Architectures Extended from Transfer Learning for Classification of Rice Leaf Diseases

Nguyễn, H. T., Quach, Q. T., Tran, C. L. H., & Luong, H. H. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-08530-7_66 · Cited by 5

Deep Learning Approach for Liver Tumor Diagnosis

Makram, M., Elhemeily, M. H. H., & Mohammed, A. (2023) · Intelligent Methods, Systems, and Applications (IMSA) · doi:10.1109/imsa58542.2023.10217588 · Cited by 5

A review of in-memory computing for machine learning: architectures, options

Snåšel, V., Dang, T. K., Kueng, J., & Kong, L. (2023) · International Journal of Web Information Systems · doi:10.1108/ijwis-08-2023-0131 · Cited by 6

Comparison Study on Convolution Neural Network (CNN) Techniques for Image Classification

Zainorzuli, S. M., Abdullah, S. A. C., Abidin, H. Z., & Ruslan, F. A. (2022) · Journal of Electrical & Electronic Systems Research · doi:10.24191/jeesr.v20i1.002 · Cited by 6

Adaptive High-order Variation De-noising Method for Edge Detection with Wavelet Coefficients

Liu, C., & Wang, A. (2023) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2023.02.007 · Cited by 5

Protein–Protein Interface Topology as a Predictor of Secondary Structure and Molecular Function Using Convolutional Deep Learning

Bouvier, B. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00644 · Cited by 5

Intelligent Waste Sorting Bin for Recyclable Municipal Solid Waste

Md, R., Sayed, R. H., & Kowshik, A. K. (2021) · International Conference on Automation, Control and Mechatronics for Industry 4.0 (ACMI) · doi:10.1109/acmi53878.2021.9528157 · Cited by 5

New Method of Internal Type-2 Fuzzy-Based CNN for Image Classification

Murugeswari, P., & Vijayalakshmi, S. (2020) · International Journal of Fuzzy Logic and Intelligent Systems · doi:10.5391/ijfis.2020.20.4.336 · Cited by 5

Wearable damaged clothing fabric image recognition system based on image restoration algorithm

Xu, L., & Zhang, L. (2024) · Systems and Soft Computing · doi:10.1016/j.sasc.2024.200140 · Cited by 4

Deep Learning for Image Processing and Reconstruction to Enhance LED-Based Photoacoustic Imaging

Sivasubramanian, K., & Xing, L. (2020) · Progress in optical science and photonics · doi:10.1007/978-981-15-3984-8_9 · Cited by 4

Comparison of Two Objects Classification Techniques using Hidden Markov Models and Convolutional Neural Networks

Sarmiento, C., & Savage, J. (2020) · Informatics and Automation · doi:10.15622/ia.2020.19.6.4 · Cited by 4

SUT: a new multi-purpose synthetic dataset for Farsi document image analysis

Shabaninia, E., Eslami, F. S., Afkari-Fahandari, A., & Nezamabadi–pour, H. (2023) · International Conference on Computer and Knowledge Engineering · doi:10.1109/iccke60553.2023.10326243 · Cited by 4

Blood Cell Detection Using Deep Learning on Mobile Platforms

Jain, N., & Khan, F. H. (2023) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci62032.2023.00211 · Cited by 4

Neural Network Approach for Modelling and Compensation of Local Surface-Tilting-Dependent Topography Measurement Errors in Coherence Scanning Interferometry

Gao, S., Li, Z., & Brand, U. (2024) · Metrology · doi:10.3390/metrology4030027 · Cited by 4

Fine Tuning Swin Transformer Based Pretrained Model for Microscopic Fungi Images Classification

Anggara, M. G., Hindasyah, A., Amda, J. M., & Waskita, A. A. (2024) · International Conference on Computer, Control, Informatics and its Applications · doi:10.1109/ic3ina64086.2024.10732255 · Cited by 4

Towards real-time and real-life image classification and detection using CNN: a review of practical applications requirements, algorithms, hardware and current trends

Ilas, M.-E., & Ilas, C. (2020) · IEEE International Symposium for Design and Technology in Electronic Packaging (SIITME) · doi:10.1109/siitme50350.2020.9292253 · Cited by 4

A pre-trained model vs dedicated convolution neural networks for emotion recognition

Nawaf, A. Y., & Jasim, W. M. (2022) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v13i1.pp1123-1133 · Cited by 4

Spatial oblivion channel attention targeting intra-class diversity feature learning

Han, H., Zhang, Q., Li, F., & Du, Y. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.07.032 · Cited by 4

Toward an Augmented and Explainable Machine Learning Approach for Classification of Defective Nanomaterial Patches

Ieracitano, C., Mammone, N., Paviglianiti, A., & Morabito, F. C. (2021) · Proceedings of the international neural networks society · doi:10.1007/978-3-030-80568-5_21 · Cited by 4

Investigation for the Need of Traditional Data-Preprocessing when Applying Artificial Neural Networks to FMCW-Radar Data

Valtl, J., Mendez, J., Mauro, G., Cabrera-Gutiérrez, A. J., & Іссаков, В. (2022) · International Conference on Systems, Signals and Image Processing · doi:10.1109/iwssip55020.2022.9854472 · Cited by 4

Convolutional Neural Networks from Image MarkersPreprint

Benato, B. C., de Souza, I. E., Galvão, F. L., & Falcão, A. X. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.12108 · Cited by 7

BE-NPU: A Bandwidth-Efficient Neural Processing Unit With Adaptive Processing Schemes for Reduced Off-Chip Bandwidth Demand

Bai, Y., Zhang, X., Wang, Q., Li, Y., Du, Y., & Du, L. (2025) · IEEE Transactions on Computers · doi:10.1109/tc.2025.3558579 · Cited by 3

Structure Sensitive and Semantic Alignment Synergistic Enhancement Network for Remote Sensing Change Detection

Wang, W., Ren, H., Wang, X., & Li, J. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3649011 · Cited by 3

Exploring the Role of Material Science in Advancing Quantum Machine Learning: A Scientometric Study

Tomar, M., Prajapat, S., Kumar, D., Kumar, P., Kumar, R., & Vasilakos, A. V. (2025) · Mathematics · doi:10.3390/math13060958 · Cited by 5

A New Method of Interval Type-2 Fuzzy-Based CNN for Image Classification

Murugeswari, P., & Vijayalakshmi, S. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-33-6862-0_57 · Cited by 3

Automated Truck Taxonomy Classification Using Deep Convolutional Neural Networks

Almutairi, A., He, P., Rangarajan, A., & Ranka, S. (2022) · International Journal of Intelligent Transportation Systems Research · doi:10.1007/s13177-022-00306-4 · Cited by 8

Predicting age and gender from network telemetry: Implications for privacy and impact on policy

Kuang, L., Pobbathi, S., Mansury, Y. S., Shapiro, M. A., & Gurbani, V. K. (2022) · PLoS ONE · doi:10.1371/journal.pone.0271714 · Cited by 5

An Effective Hybrid Convolutional-Modified Extreme Learning Machine in Early Stage Diabetic Retinopathy

(2023) · International journal of intelligent engineering and systems · doi:10.22266/ijies2023.0430.32 · Cited by 3

Effects of Face Image Degradation on Recognition with Vision Transformers: Review and Case Study

Al-Otaiby, N. F., & El-Alfy, E.-S. M. (2023) · International Conference on Computing and Information Technology (ICCIT) · doi:10.1109/iccit58132.2023.10273970 · Cited by 3

BCIM: Efficient Implementation of Binary Neural Network Based on Computation in Memory

Zahedi, M., Shahroodi, T., Escuin, C., Gaydadjiev, G. N., Wong, S., & Hamdioui, S. (2024) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2024.3406628 · Cited by 4

Classification Taxonomies Genus of 90 Animals Using Transfer Learning Resnet-152

Saputro, S. N., Adhinata, F. D., & Athiyah, U. (2024) · CommIT (Communication and Information Technology) Journal · doi:10.21512/commit.v18i1.9482 · Cited by 3

Automated Blood Cell Identification, Counting, and Sub type classification using Deep Learning

Kishore, V. V., Kalpana, V., & Nagendra, R. (2024) · International Conference on Automation, Computing and Renewable Systems (ICACRS) · doi:10.1109/icacrs62842.2024.10841756 · Cited by 2

A Channeled Multilayer Perceptron as Multi-Modal Approach for Two Time-Frames Algo-Trading Strategy

Fikri, N., Moussaid, K., Rida, M., Omri, A. E., & Abghour, N. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.0130259 · Cited by 2

No-Reference Image Quality Assessment based on Quality Awareness Feature and Multi-task Training

Lai, L., Chu, J. H., & Leng, L. (2022) · Journal of Multimedia Information System · doi:10.33851/jmis.2022.9.2.75 · Cited by 2

Optimizing Video Analytics Deployment for In-Flight Cabin Readiness Verification

Elordi, U., Aranjuelo, N., Unzueta, L., Apellániz, J. L., & Arganda‐Carreras, I. (2023) · IEEE Access · doi:10.1109/access.2023.3309050 · Cited by 3

Deep Learning Based World Coin Currency Detection

Jahan, Z., Nazia, P., Agrawal, A. P., Choudhary, A., Raj, G., & Deraman, A. (2023) · Algorithms for intelligent systems · doi:10.1007/978-981-99-1620-7_35 · Cited by 2

Fault Detection in Cascaded H-Bridge Inverters using Spectrogram Analysis and Convolutional Neural Networks

Rokocakau, S., Tresca, G., Cirrincione, G., Zanchetta, P., Kumar, R., Cirrincione, M., & Frosini, L. (2023) · International Aegean Conference on Electrical Machines and Power Electronics & International Conference on Optimization of Electrical and Electronic Equipment (ACEMP-OPTIM) · doi:10.1109/acemp-optim57845.2023.10287070 · Cited by 3

CVII: Enhancing Interpretability in Intelligent Sensor Systems via Computer Vision Interpretability Index

Mohammadi, H., Thirunarayan, K., & Chen, L. (2023) · Sensors · doi:10.3390/s23249893 · Cited by 2

Analysis of Convolutional Neural Network Models for Classifying the Quality of Dried Chili Peppers (Capsicum Annuum L)

Navarro-Solís, D., Guerrero-Méndez, C., Saucedo-Anaya, T., López-Betancur, D., Silva, L., Robles-Guerrero, A., & Gómez-Jiménez, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-51940-6_10 · Cited by 2

Exploring the Potential of Convolution Neural Network Based Image Classification

Sharma, R., Bashir, S., Tiwary, V. N., & Kumar, S. (2023) · DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIHEI) · doi:10.1109/idicaiei58380.2023.10406528 · Cited by 2

Tetris: Accelerating Sparse Convolution by Exploiting Memory Reuse on GPU

Liu, X., Zheng, X., Yang, H., Luan, Z., & Qian, D. (2024) · ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming · doi:10.1145/3627535.3638471 · Cited by 2

Robust Class-Conditional Distribution Alignment for Partial Domain Adaptation

Choudhuri, S., & Sen, A. (2023) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/ieeeconf59524.2023.10477051 · Cited by 2

Dendritic Learning-Based Feature Fusion for Deep Networks

Song, Y., Liu, Z., Zhang, Z., Tang, J., Lei, Z., & Gao, S. (2024) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2024edl8021 · Cited by 2

Deep Learning in Biomedical Applications of Raman Spectroscopy

Anjikar, A., Rao, N. P., Paneerselvam, R., Jayaramulu, K., Narayana, C., Yamamoto, T., & Noothalapati, H. (2024) · Biological and medical physics, biomedical engineering · doi:10.1007/978-981-97-5345-1_9 · Cited by 2

Affine-Transformation-Invariant Image Classification by Differentiable Arithmetic Distribution Module

Tan, Z., Dong, G., Zhao, C., & Basu, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-82475-3_6 · Cited by 6

Animal Fur Recognition Algorithm Based on Feature Fusion Network

Liu, P., Lei, T., Xiang, Q., Wang, Z., & Wang, J. (2022) · Journal of Multimedia Information System · doi:10.33851/jmis.2022.9.1.1 · Cited by 2

Modeling of quantum-like cognitive phenomena by the Fourier-holography technique under the choice of alternatives

Pavlov, A. V. (2021) · Computer Optics · doi:10.18287/2412-6179-co-830 · Cited by 2

Covid-19 Recognition by Chest CT and Deep Learning

Yang, L., & Lima, D. (2022) · ICST Transactions on e-Education and e-Learning · doi:10.4108/eai.7-1-2022.172812 · Cited by 2

PSI Analysis of Adversarial-Attacked DCNN Models

Lee, Y., & Kim, J. (2023) · Applied Sciences · doi:10.3390/app13179722 · Cited by 2

Explaining DNN Based Facial Expression Classifications

ter Burg, K. (2021) · Utrecht University Repository (Utrecht University) · Cited by 2

Researches advanced in image recognization based on deep learning

Li, Z. (2023) · Applied and Computational Engineering · doi:10.54254/2755-2721/5/20230657 · Cited by 1

Hardware Implementation of Optimized LeNet-5

Ju, J., & Liu, C. (2022) · International Conference on Computational Modeling, Simulation and Data Analysis (CMSDA) · doi:10.1109/cmsda58069.2022.00010 · Cited by 1

Fusing Deep Immuno Net and Random Forest to Improve the Detection of Dengue Fever: A Computational Strategy for Prompt and Precise Diagnosis

Vasanthi, N. (2024) · Journal of Electrical Systems · doi:10.52783/jes.2522 · Cited by 1

KDViT: COVID-19 diagnosis on CT-scans with knowledge distillation of vision transformer

Lim, Y. J., Lim, K. M., Chang, R. K. Y., & Lee, C. P. (2024) · Automatika · doi:10.1080/00051144.2024.2349416 · Cited by 1

Exploring Data Efficiency in Image Restoration: A Gaussian Denoising Case Study

Yin, Z., Ma, M., Lin, G., & Zheng, Y. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680603 · Cited by 1

Classification of images using Gaussian copula model in empirical cumulative distribution function space

Indratno, S. W., Winarni, S., & Sari, K. N. (2024) · PLoS ONE · doi:10.1371/journal.pone.0309884 · Cited by 1

Redes neuronales convolucionales para la clasificación de la mancha negra en los cítricos

Namuche, A. A. H., Alvarado, B. E., & García-Estrella, C. W. (2025) · Sapiens International Multidisciplinary Journal · doi:10.71068/4x9sw526 · Cited by 1

Multifunctional cells based neural architecture search for plant images classification

Huang, L., Qin, X., & Yang, T. (2025) · Scientific Reports · doi:10.1038/s41598-025-11829-7 · Cited by 1

Breast Cancer Classification in Deep Ultraviolet Fluorescence Images Using a Patch-Level Vision Transformer Framework

Afshin, P., Helminiak, D. S., Lu, T., Yen, T. W. F., Jorns, J. M., Patton, M. D., Yu, B., & Ye, D. H. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11253275 · Cited by 1

Plant disease diagnosis: A survey and comparative analysis of current techniques

Nayak, B., Dash, S., Bisoyi, A., & Dash, D. P. (2026) · Next research. · doi:10.1016/j.nexres.2026.101596 · Cited by 3

An 8-bit Fixed Point Quantization Method for Sparse MobileNetV2

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