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

Deep Residual Learning for Image Recognition

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Very Deep Convolutional Networks for Large-Scale Image Recognition

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ImageNet classification with deep convolutional neural networks

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Adam: A Method for Stochastic Optimization

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

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

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ImageNet: A large-scale hierarchical image database

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Gradient-based learning applied to document recognition

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Review of Image Classification Algorithms Based on Convolutional Neural Networks

Chen, L., Li, S., Bai, Q., Yang, J., Jiang, S., & Miao, Y. (2021) · Remote Sensing · doi:10.3390/rs13224712 · Cited by 893

Machine Learning for Structural Engineering: A State-of-the-Art Review

Thai, H.-T. (2022) · Structures · doi:10.1016/j.istruc.2022.02.003 · Cited by 748

Deep Learning for Medical Image-Based Cancer Diagnosis

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A Novel Image Classification Approach via Dense-MobileNet Models

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A CBAM Based Multiscale Transformer Fusion Approach for Remote Sensing Image Change Detection

Wang, W., Tan, X., Zhang, P., & Wang, X. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3198517 · Cited by 213

A Survey for Cervical Cytopathology Image Analysis Using Deep Learning

Rahaman, M. M., Li, C., Wu, X., Yao, Y., Hu, Z., Jiang, T., Li, X., & Qi, S. (2020) · IEEE Access · doi:10.1109/access.2020.2983186 · Cited by 156

A comprehensive review of Binary Neural Network

Yuan, C., & Agaian, S. С. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10464-w · Cited by 164

Recent advances in plant disease severity assessment using convolutional neural networks

Shi, T., Liu, Y., Zheng, X., Hu, K., Huang, H. M., Liu, H., & Huang, H. (2023) · Scientific Reports · doi:10.1038/s41598-023-29230-7 · Cited by 147

A survey on deep learning-based monocular spacecraft pose estimation: Current state, limitations and prospects

Pauly, L., Rharbaoui, W., Shneider, C., Rathinam, A., Gaudillière, V., & Aouada, D. (2023) · Acta Astronautica · doi:10.1016/j.actaastro.2023.08.001 · Cited by 121

Distribution Alignment Using Complement Entropy Objective and Adaptive Consensus-Based Label Refinement For Partial Domain Adaptation

Choudhuri, S., Adeniye, S., & Sen, A. (2023) · Artificial Intelligence and Applications · doi:10.47852/bonviewaia2202524 · Cited by 121

Artificial neural networks applications in partially shaded PV systems

Olabi, A. G., Abdelkareem, M. A., Semeraro, C., Radi, M. A., Rezk, H., Muhaisen, O., Al-Isawi, O. A., & Sayed, E. T. (2022) · Thermal Science and Engineering Progress · doi:10.1016/j.tsep.2022.101612 · Cited by 95

Machine Learning Techniques for THz Imaging and Time-Domain Spectroscopy

Park, H., & Son, J. (2021) · Sensors · doi:10.3390/s21041186 · Cited by 93

Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature

Mohammed, F. A., Tune, K. K., Assefa, B. G., Jett, M., & Muhie, S. Y. (2024) · Machine Learning and Knowledge Extraction · doi:10.3390/make6010033 · Cited by 77

A Generic Approach for Wheat Disease Classification and Verification Using Expert Opinion for Knowledge-Based Decisions

Haider, W., Rehman, A.-U., Durrani, M., & Rehman, S. U. (2021) · IEEE Access · doi:10.1109/access.2021.3058582 · Cited by 84

A New Image Classification Approach via Improved MobileNet Models with Local Receptive Field Expansion in Shallow Layers

Wang, W., Hu, Y., Zou, T., Liu, H., Wang, J., & Wang, X. (2020) · Computational Intelligence and Neuroscience · doi:10.1155/2020/8817849 · Cited by 80

Computer-Aided Diagnosis of Alzheimer’s Disease through Weak Supervision Deep Learning Framework with Attention Mechanism

Liang, S., & Gu, Y. (2020) · Sensors · doi:10.3390/s21010220 · Cited by 73

Deep learning serves traffic safety analysis: A forward‐looking review

Razi, A., Chen, X., Li, H., Wang, H., Russo, B. J., Chen, Y., & Yu, H. (2022) · IET Intelligent Transport Systems · doi:10.1049/itr2.12257 · Cited by 83

Deep Learning for Earthquake Disaster Assessment: Objects, Data, Models, Stages, Challenges, and Opportunities

Jia, J. C., & Ye, W. (2023) · Remote Sensing · doi:10.3390/rs15164098 · Cited by 80

ACE-CNN: Approximate Carry Disregard Multipliers for Energy-Efficient CNN-Based Image Classification

Shakibhamedan, S., Amirafshar, N., Baroughi, A. S., Shahhoseini, H. S., & TaheriNejad, N. (2024) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2024.3369230 · Cited by 62

Development of residual learning in deep neural networks for computer vision: A survey

Xu, G., Wang, X., Wu, X., Leng, X., & Xu, Y. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109890 · Cited by 104

Deep Learning Based Image Processing for Robot Assisted Surgery: A Systematic Literature Survey

Hussain, S. M., Brunetti, A., Lucarelli, G. T., Memeo, R., Bevilacqua, V., & Buongiorno, D. (2022) · IEEE Access · doi:10.1109/access.2022.3223704 · Cited by 57

Zero shot objects classification method of side scan sonar image based on synthesis of pseudo samples

Li, C., Ye, X., Cao, D., Hou, J., & Yang, H. (2020) · Applied Acoustics · doi:10.1016/j.apacoust.2020.107691 · Cited by 55

Benign and Malignant Breast Tumor Classification in Ultrasound and Mammography Images via Fusion of Deep Learning and Handcraft Features

Cruz-Ramos, C., García-Avila, O., Almaraz-Damian, J. A., Ponomaryov, V. I., Reyes-Reyes, R., & Sadovnychiy, S. (2023) · Entropy · doi:10.3390/e25070991 · Cited by 55

Nonlinear Functional Modeling Using Neural Networks

Rao, A. R., & Reimherr, M. L. (2023) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2023.2165498 · Cited by 53

Potato Surface Defect Detection Based on Deep Transfer Learning

Wang, C., & Xiao, Z. (2021) · Agriculture · doi:10.3390/agriculture11090863 · Cited by 53

Deep Learning Beehive Monitoring System for Early Detection of the Varroa Mite

Voudiotis, G., Moraiti, A., & Kontogiannis, S. I. (2022) · Signals · doi:10.3390/signals3030030 · Cited by 51

Complete Blood Cell Detection and Counting Based on Deep Neural Networks

Lee, S., Chen, P.-Y., & Lin, J.-W. (2022) · Applied Sciences · doi:10.3390/app12168140 · Cited by 56

Woodland Labeling in Chenzhou, China, via Deep Learning Approach

Wang, W., Yang, Y., Li, J., Hu, Y., Luo, Y., & Wang, X. (2020) · International Journal of Computational Intelligence Systems · doi:10.2991/ijcis.d.200910.001 · Cited by 47

Using a multi-convolutional neural network to automatically identify small-sample tea leaf diseases

Hu, G., & Fang, M. (2022) · Sustainable Computing Informatics and Systems · doi:10.1016/j.suscom.2022.100696 · Cited by 44

Few-Shot Learning for Plant-Disease Recognition in the Frequency Domain

Lin, H., Tse, R. T., Tang, S.-K., Qiang, Z., & Pau, G. (2022) · Plants · doi:10.3390/plants11212814 · Cited by 42

Fine-tuning pre-trained neural networks for medical image classification in small clinical datasets

Spolaôr, N., Lee, H., Mendes, A. I., Nogueira, C. V., Parmezan, A. R. S., Takaki, W. S. R., Coy, C. S. R., Wu, F. C., & Fonseca-Pinto, R. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16529-w · Cited by 42

Identification and measurement of tropical tuna species in purse seiner catches using computer vision and deep learning

Lekunberri, X., Ruiz, J., Quincoces, I., Dornaika, F., Arganda‐Carreras, I., & Fernandes, J. A. (2021) · Ecological Informatics · doi:10.1016/j.ecoinf.2021.101495 · Cited by 39

Deep Learning for Image-Based Plant Growth Monitoring: A Review

Tong, Y.-S., Lee, T.-H., & Yen, K. S. (2022) · International Journal of Engineering and Technology Innovation · doi:10.46604/ijeti.2022.8865 · Cited by 43

Diagnosing Atrial Septal Defect from Electrocardiogram with Deep Learning

Mori, H., Inai, K., Sugiyama, H., & Muragaki, Y. (2021) · Pediatric Cardiology · doi:10.1007/s00246-021-02622-0 · Cited by 39

Using CFW-Net Deep Learning Models for X-Ray Images to Detect COVID-19 Patients

Wang, W., Liu, H., Li, J., Nie, H., & Wang, X. (2020) · International Journal of Computational Intelligence Systems · doi:10.2991/ijcis.d.201123.001 · Cited by 38

Seismic Impedance Inversion Based on Residual Attention Network

Wu, B., Xie, Q., & Wu, B. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3193563 · Cited by 38

An Efficient Deep Learning Based Method for Speech Assessment of Mandarin-Speaking Aphasic Patients

Mahmoud, S. S., Kumar, A., Tang, Y., Li, Y., Gu, X., Fu, J., & Fang, Q. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3011104 · Cited by 36

Experiments on image data augmentation techniques for geological rock type classification with convolutional neural networks

Tatar, A., Haghighi, M., & Zeinijahromi, A. (2024) · Journal of Rock Mechanics and Geotechnical Engineering · doi:10.1016/j.jrmge.2024.02.015 · Cited by 38

Proposed Smart Monitoring System for the Detection of Bee Swarming

Voudiotis, G., Kontogiannis, S. I., & Pikridas, C. (2021) · Inventions · doi:10.3390/inventions6040087 · Cited by 34

A comprehensive survey of intestine histopathological image analysis using machine vision approaches

Jing, Y., Li, C., Du, T., Jiang, T., Sun, H., Yang, J., Shi, L., Gao, M., Grzegorzek, M., & Li, X. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107388 · Cited by 40

Performance Evaluation of Deep Learning Classification Network for Image Features

Li, Q., Yang, Y., Guo, Y., Li, W., Liu, Y., Liu, H., & Kang, Y. (2021) · IEEE Access · doi:10.1109/access.2020.3048956 · Cited by 37

Deep learning-based application for multilevel sentiment analysis of Indonesian hotel reviews

Kusumaningrum, R., Nisa, I. Z., Jayanto, R., Nawangsari, R. P., & Wibowo, A. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e17147 · Cited by 33

Detecting COVID-19 Patients in X-Ray Images Based on MAI-Nets

Wang, W., Huang, X. M., Li, J., Zhang, P., & Wang, X. (2021) · International Journal of Computational Intelligence Systems · doi:10.2991/ijcis.d.210518.001 · Cited by 31

Inception 1D-convolutional neural network for accurate prediction of electrical insulator leakage current from environmental data during its normal operation using long-term recording

Bueno-Barrachina, J.-M., Ye-Lin, Y., Nieto-del-Amor, F., & Fuster-Roig, V. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105799 · Cited by 31

EDNC: Ensemble Deep Neural Network for COVID-19 Recognition

Yang, L., Wang‎, S., & Zhang, Y. (2022) · Tomography · doi:10.3390/tomography8020071 · Cited by 30

A Modified Convolutional Neural Networks For MRI-based Images For Detection and Stage Classification Of Alzheimer Disease

Alshammari, M., & Mezher, M. A. (2021) · National Computing Colleges Conference (NCCC) · doi:10.1109/nccc49330.2021.9428810 · Cited by 29

Performance Evaluation of Machine Learning Frameworks for Aphasia Assessment

Mahmoud, S. S., Kumar, A., Li, Y., Tang, Y., & Fang, Q. (2021) · Sensors · doi:10.3390/s21082582 · Cited by 27

Automatic borehole fracture detection and characterization with tailored Faster R-CNN and simplified Hough transform

Han, S., Xiao, X., Song, B., Guan, T., Zhang, Y., & Lyu, M. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107024 · Cited by 27

Search for or Navigate to? Dual Adaptive Thinking for Object Navigation

Dang, R., Wang, L., He, Z., Su, S., Tang, J., Liu, C., & Chen, Q. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00758 · Cited by 32

Image Object Recognition via Deep Feature-Based Adaptive Joint Sparse Representation

Wang, W., Tang, C., Wang, X., Luo, Y., Hu, Y., & Li, J. (2019) · Computational Intelligence and Neuroscience · doi:10.1155/2019/8258275 · Cited by 26

Visual Recognition of Traffic Signs in Natural Scenes Based on Improved RetinaNet

Liu, S., Cai, T., Tang, X., Zhang, Y., & Wang, C. (2022) · Entropy · doi:10.3390/e24010112 · Cited by 25

A novel model for efficient cluster head selection in mobile WSNs using residual energy and neural networks

Jalili, A., Gheisari, M., Alzubi, J. A. A., Fernández‐Campusano, C., Kamalov, F., & Moussa, S. (2024) · Measurement Sensors · doi:10.1016/j.measen.2024.101144 · Cited by 23

The Immune Depth Presentation Convolutional Neural Network Used for Oil and Gas Pipeline Fault Diagnosis

Xu, J., & Yu, X. Y. (2024) · IEEE Access · doi:10.1109/access.2024.3358208 · Cited by 22

Attention-based CNNs for Image Classification: A Survey

Zheng, M., Xu, J., Shen, Y., Tian, C., Li, J., Fei, L., Zong, M., & Liu, X. (2022) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2171/1/012068 · Cited by 22

Semi-Self-Supervised Learning for Semantic Segmentation in Images with Dense Patterns

Najafian, K., Ghanbari, A., Kish, M. S., Eramian, M., Shirdel, G. H., Stavness, I., Jin, L., & Maleki, F. (2023) · Plant Phenomics · doi:10.34133/plantphenomics.0025 · Cited by 22

Comparison of CNN’s Architecture GoogleNet, AlexNet, VGG-16, Lenet -5, Resnet-50 in Arabic Handwriting Pattern Recognition

Nugraha, G. S., Darmawan, M. I., & Dwiyansaputra, R. (2023) · Kinetik Game Technology Information System Computer Network Computing Electronics and Control · doi:10.22219/kinetik.v8i2.1667 · Cited by 22

One-Shot Neural Architecture Search by Dynamically Pruning Supernet in Hierarchical Order

Zhang, J., Li, D., Wang, L., & Zhang, L. (2021) · International Journal of Neural Systems · doi:10.1142/s0129065721500295 · Cited by 22

Application of Deep Learning for Imaging‐Based Stream Gaging

Boomen, R. L. V., Yu, Z., & Liao, Q. (2021) · Water Resources Research · doi:10.1029/2021wr029980 · Cited by 23

A Deep-Convolutional-Neural-Network-Based Semi-Supervised Learning Method for Anomaly Crack Detection

Gao, X., Huang, C., Teng, S., & Chen, G. (2022) · Applied Sciences · doi:10.3390/app12189244 · Cited by 20

Demystifying Bitcoin Address Behavior via Graph Neural Networks

Huang, Z., Huang, Y., Qian, P., Chen, J., & He, Q. (2023) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde55515.2023.00137 · Cited by 23

Research on food safety sampling inspection system based on deep learning

Chen, T.-C., & Yu, S. (2021) · Food Science and Technology · doi:10.1590/fst.29121 · Cited by 20

Urban infrastructure audit: an effective protocol to digitize signalized intersections by mining street view images

Li, X., Ning, H., Huang, X., Dadashova, B., Kang, Y., & Ma, A. (2021) · Cartography and Geographic Information Science · doi:10.1080/15230406.2021.1992299 · Cited by 19

Efficient Malaria Parasite Detection From Diverse Images of Thick Blood Smears for Cross-Regional Model Accuracy

Zhong, Y., Dan, Y., Cai, Y., Lin, J., Huang, X., Mahmoud, O., Hald, E. S., Kumar, A., Fang, Q., & Mahmoud, S. S. (2023) · IEEE Open Journal of Engineering in Medicine and Biology · doi:10.1109/ojemb.2023.3328435 · Cited by 19

Automatic CNN-Based Enhancement of 360° Video Experience With Multisensorial Effects

Sexton, J. P., Simiscuka, A. A., McGuinness, K., & Muntean, G. (2021) · IEEE Access · doi:10.1109/access.2021.3115701 · Cited by 19

Remote sensing landslide target detection method based on improved Faster R-CNN

Yang, D., & Mao, Y. (2022) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.16.044521 · Cited by 18

CNN Based Image Classification of Malicious UAVs

Brown, J., Gharineiat, Z., & Raj, N. (2022) · Applied Sciences · doi:10.3390/app13010240 · Cited by 18

Brief Survey of Single Image Super-Resolution Reconstruction Based on Deep Learning Approaches

Wang, W., Hu, Y., Luo, Y., & Zhang, T. (2020) · Sensing and Imaging · doi:10.1007/s11220-020-00285-4 · Cited by 20

Self-Supervised Bi-Channel Transformer Networks for Computer-Aided Diagnosis

Gong, R., Han, X., Wang, J., Ying, S., & Shi, J. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3153902 · Cited by 18

Neural Network-based Pattern Recognition in the Framework of Edge Computing

Ning, J. (2024) · Romanian Journal of Information Science and Technology · doi:10.59277/romjist.2024.1.08 · Cited by 17

High-Resolution Radar Target Recognition via Inception-Based VGG (IVGG) Networks

Wang, W., Zhang, C., Tian, J., Wang, X., Ou, J., Zhang, J., & Li, J. (2020) · Computational Intelligence and Neuroscience · doi:10.1155/2020/8893419 · Cited by 17

Classification of error-related potentials evoked during stroke rehabilitation training

Kumar, A., Pirogova, E., Mahmoud, S. S., & Fang, Q. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac1d32 · Cited by 17

A SAR Image Target Recognition Approach via Novel SSF-Net Models

Wang, W., Zhang, C., Tian, J., Ou, J., & Li, J. (2020) · Computational Intelligence and Neuroscience · doi:10.1155/2020/8859172 · Cited by 17

Pattern Mathematical Model for Fingerprint Security Using Bifurcation Minutiae Extraction and Neural Network Feature Selection

Alsharman, N., Saaidah, A., Almomani, O., Jawarneh, I., & Al-Qaisi, L. (2022) · Security and Communication Networks · doi:10.1155/2022/4375232 · Cited by 16

Classification of the Relationship Between Mandibular Third Molar and Inferior Alveolar Nerve Based on Generated Mask Images

Joo, Y., Moon, S., & Choi, C. (2023) · IEEE Access · doi:10.1109/access.2023.3302271 · Cited by 15

Multimodal movie genre classification using recurrent neural network

Behrouzi, T., Toosi, R., & Akhaee, M. A. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13418-6 · Cited by 19

Deep convolutional cross-connected kernel mapping support vector machine based on SelectDropout

Wang, Q., Liu, Z., Zhang, T., Alasmary, H., Waqas, M. M., Halim, Z., & Li, Y. (2023) · Information Sciences · doi:10.1016/j.ins.2023.01.004 · Cited by 15

CDF‐net: A convolutional neural network fusing frequency domain and spatial domain features

Yang, A., Li, M., Wu, Z., He, Y., Qiu, X., Song, Y. C., Du, W., & Gou, Y. (2023) · IET Computer Vision · doi:10.1049/cvi2.12167 · Cited by 18

Foreign objects detection using deep learning techniques for graphic card assembly line

Kuo, R. J., & Nursyahid, F. F. (2022) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-022-01980-7 · Cited by 14

Fusion of Convolution Neural Network and Visual Transformer for Lithology Identification Using Tunnel Face Images

Tong, J., Xiang, L., Zhang, A. A., Miao, X. F., Wang, M., & Ye, P. (2024) · Journal of Computing in Civil Engineering · doi:10.1061/jccee5.cpeng-5997 · Cited by 13

Analysis of Deep Learning Architectures for Object Detection - A Critical Review

Pandiya, M., Dassani, S., & Mangalraj, P. (2020) · IEEE-HYDCON · doi:10.1109/hydcon48903.2020.9242776 · Cited by 13

Paper-Fiber-Activated Triplet Excitons of Carbon Nanodots for Time-Resolved Anti-counterfeiting Signature with Artificial Intelligence Authentication

Mao, X., Liu, K.-K., Cao, Q., Song, S.-Y., Liang, Y., Hu, Y., Chang, S., Liao, J., & Shan, C. (2023) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.3c00414 · Cited by 13

An Overview of Medical Imaging Techniques for Knee Osteoarthritis Disease

Vashishtha, A., & Acharya, A. K. (2021) · Biomedical & Pharmacology Journal · doi:10.13005/bpj/2192 · Cited by 13

Classification of Microbiome Data from Type 2 Diabetes Mellitus Individuals with Deep Learning Image Recognition

Pfeil, J., Siptroth, J., Pospisil, H., Frohme, M., Hufert, F. T., Moskalenko, O., Yateem, M., & Nechyporenko, A. S. (2023) · Big Data and Cognitive Computing · doi:10.3390/bdcc7010051 · Cited by 13

Utilizing excitation-emission matrix fluorescence spectroscopy and convolutional neural networks for dark tea brand and aging period identification

Tan, X. L., He, J., Li, H., Jiang, J., He, Z., Guo, J., & Qiu, B. (2024) · Journal of Food Composition and Analysis · doi:10.1016/j.jfca.2024.106458 · Cited by 12

Dynamic ensemble selection of convolutional neural networks and its application in flower classification

Wang, Z., Wang, K., Wang, X., Pan, S., & Qiao, X. (2022) · International journal of agricultural and biological engineering · doi:10.25165/j.ijabe.20221501.6313 · Cited by 12

A COVID‐19 CXR image recognition method based on MSA‐DDCovidNet

Wang, W., Huang, W., Wang, X., Zhang, P., & Zhang, N. (2022) · IET Image Processing · doi:10.1049/ipr2.12474 · Cited by 12

Improved Classification Model using CNN for Detection of Alzheimer’s Disease

Davuluri, R., & Ragupathy, R. (2022) · Journal of Computer Science · doi:10.3844/jcssp.2022.415.425 · Cited by 12

Retinal disease classification based on optical coherence tomography images using convolutional neural networks

Stanojević, M., Drašković, D., & Nikolić, B. (2022) · Journal of Electronic Imaging · doi:10.1117/1.jei.32.3.032004 · Cited by 14

Deep learning‐based robust hybrid approaches for brain tumor classification in magnetic resonance images

Krishnasamy, N., & Ponnusamy, T. (2023) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22974 · Cited by 11

A COVID-19 CXR Image Recognition Method Based on Deep Transfer Learning

An, J., Zhang, N., Mahmoud, W. H., & Denis, M. F. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650886 · Cited by 11

Fast Evaluation of Unhealthy and Healthy Neonates Using Hyperspectral Features on 700-850 Nm Wavelengths, ROI Extraction, and 3D-CNN

Cıhan, M., Ceylan, M., Soylu, H., & Konak, M. (2021) · IRBM · doi:10.1016/j.irbm.2021.06.009 · Cited by 11

Automated Cerebral Infarct Detection on Computed Tomography Images Based on Deep Learning

Peng, S., Chen, Y., Yang, J.-Y., Wang, K., & Tsai, J. (2022) · Biomedicines · doi:10.3390/biomedicines10010122 · Cited by 11

Artificial neural network to estimate the refractive index of a liquid infiltrating a chiral sculptured thin film

McAtee, P. D., Bukkapatnam, S., & Lakhtakia, A. (2019) · Journal of Nanophotonics · doi:10.1117/1.jnp.13.046006 · Cited by 13

The Study of the Effectiveness of Advanced Algorithms for Learning Neural Networks Based on FPGA in the Musical Notation Classification Task

Sokół, S., Pawuś, D., Majewski, P., & Krok, M. (2022) · Applied Sciences · doi:10.3390/app12199829 · Cited by 10

A Survey on 3D Scene Graphs: Definition, Generation and Application

Bae, J., Shin, D., Ko, K., Lee, J., & Kim, U.-H. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-26889-2_13 · Cited by 10

A Convolutional Neural Network Ensemble for Flower Image Classification

Wang, Z., Wang, K., Wang, X., & Pan, S. (2020) · International Conference on Computing and Pattern Recognition (ICCPR) · doi:10.1145/3436369.3437427 · Cited by 11

SCU-Net: Semantic Segmentation Network for Learning Channel Information on Remote Sensing Images

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