Deep learning based classification of focal liver lesions with contrast-enhanced ultrasound

LIBSVM: A library for support vector machines

Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918 · see citing works

Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008

Ferlay, J., Shin, H.-R., Bray, F., Forman, D., Mathers, C., & Parkin, D. M. (2010) · International Journal of Cancer · doi:10.1002/ijc.25516 · Cited by 22,207 · see citing works

Reducing the dimensionality of data with neural networks

Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928 · see citing works

A fast learning algorithm for deep belief nets

Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336 · see citing works

Libsvm : A library for support vector machines

Ferrari, V. (2008) · Medical Entomology and Zoology · Cited by 10,124 · see citing works

Learning Deep Architectures for AI

Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 7,048 · see citing works

Proceedings of the 25th international conference on Machine learning - ICML '08

(2008) · doi:10.1145/1390156 · Cited by 6,865 · see citing works

Learning Deep Architectures for AI

Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 5,037 · see citing works

Training Products of Experts by Minimizing Contrastive Divergence

Hinton, G. E. (2002) · Neural Computation · doi:10.1162/089976602760128018 · Cited by 4,879 · see citing works

A Practical Guide to Training Restricted Boltzmann Machines

Hinton, G. E. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8_32 · Cited by 2,852 · see citing works

Deep Machine Learning - A New Frontier in Artificial Intelligence Research [Research Frontier]

Arel, I., Rose, D. C., & Karnowski, T. P. (2010) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2010.938364 · Cited by 1,128 · see citing works

Guidelines and Good Clinical Practice Recommendations for Contrast Enhanced Ultrasound (CEUS) in the Liver – Update 2012

Claudon, M., Dietrich, C. F., Choi, B. I., Cosgrove, D. O., Kudo, M., Nolsøe, C., Piscaglia, F., Wilson, S. R., Barr, R. G., Chammas, M. C., et al. (2012) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2012.09.002 · Cited by 973 · see citing works

Untitled work

(2009) · neural information processing systems · Cited by 962 · see citing works

Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis

Kim, H., & Park, H. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm134 · Cited by 865 · see citing works

Circulating Tumor Cells versus Imaging—Predicting Overall Survival in Metastatic Breast Cancer

Budd, G. T., Cristofanilli, M., Ellis, M. J., Stopeck, A., Borden, E. C., Miller, M. C., Matera, J., Repollet, M., Doyle, G. V., Terstappen, L. W. M. M., et al. (2006) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-05-1769 · Cited by 776 · see citing works

Systematic 5 Region Prostate Biopsy is Superior to Sextant Method for Diagnosing Carcinoma of the Prostate

Eskew, L. A., Bare, R. L., & McCullough, D. L. (1997) · The Journal of Urology · doi:10.1016/s0022-5347(01)65322-9 · Cited by 765 · see citing works

Cancer Screening in the United States, 2010: A Review of Current American Cancer Society Guidelines and Issues in Cancer Screening

Smith, R. A., Cokkinides, V. E., Brooks, D. D., Saslow, D., & Brawley, O. W. (2010) · CA A Cancer Journal for Clinicians · doi:10.3322/caac.20063 · Cited by 659 · see citing works

Sentinel Lymph Node Biopsy in Breast Cancer

Veronesi, U., Viale, G., Paganelli, G., Zurrida, S., Luini, A., Galimberti, V. E., Veronesi, P., Intra, M., Maisonneuve, P., Zucca, F., et al. (2010) · Annals of Surgery · doi:10.1097/sla.0b013e3181c0e92a · Cited by 554 · see citing works

Guidelines and Good Clinical Practice Recommendations for Contrast Enhanced Ultrasound (CEUS) in the Liver – Update 2012

Claudon, M., Dietrich, C. F., Choi, B. I., Cosgrove, D. O., Kudo, M., Nolsøe, C., Piscaglia, F., Wilson, S. R., Barr, R., Chammas, M. C., et al. (2012) · Ultraschall in der Medizin - European Journal of Ultrasound · doi:10.1055/s-0032-1325499 · Cited by 550 · see citing works

Characterization of Focal Liver Lesions with Contrast-specific US Modes and a Sulfur Hexafluoride–filled Microbubble Contrast Agent: Diagnostic Performance and Confidence

Quaia, E., Calliada, F., Bertolotto, M., Rossi, S. M., Garioni, L., de la Rosa, L. E. S., & Mucelli, R. P. (2004) · Radiology · doi:10.1148/radiol.2322031401 · Cited by 477 · see citing works

Deep Learning and Its Applications to Signal and Information Processing [Exploratory DSP

Yu, D., & Deng, L. (2010) · IEEE Signal Processing Magazine · doi:10.1109/msp.2010.939038 · Cited by 459 · see citing works

On the quantitative analysis of deep belief networks

Salakhutdinov, R., & Murray, I. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390266 · Cited by 411 · see citing works

Selected techniques for data mining in medicine

Nada Lavrač, A. (1999) · Artificial Intelligence in Medicine · doi:10.1016/s0933-3657(98)00062-1 · Cited by 351 · see citing works

Large-scale Learning with SVM and Convolutional for Generic Object Categorization

Huang, F. J., & LeCun, Y. (2006) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2006.164 · Cited by 320 · see citing works

Deep Machine Learning—A New Frontier in Artificial Intelligence Research

Arel, I., Rose, D. C., & Karnowski, T. P. (2010) · Cited by 314 · see citing works

Advances in Neural Information Processing Systems 21 (NIPS 2008)

Schweikert, G., Rätsch, G., Widmer, C., & Schölkopf, B. (2008) · Cited by 212 · see citing works

Synergistic Face Detection and Pose Estimation with Energy-Based Models

Osadchy, M., LeCun, Y., & Miller, M. L. (2006) · Lecture notes in computer science · doi:10.1007/11957959_10 · Cited by 211 · see citing works

Handling of Dynamic Sequences in Nuclear Medicine

Paola, R. D., Bazin, J., Aubry, F., Aurengo, A., Cavaillolès, F., Herry, J. Y., & Kahn, E. (1982) · IEEE Transactions on Nuclear Science · doi:10.1109/tns.1982.4332188 · Cited by 197 · see citing works

The use of principal components in the quantitative analysis of gamma camera dynamic studies

Barber, D. C. (1980) · Physics in Medicine and Biology · doi:10.1088/0031-9155/25/2/008 · Cited by 188 · see citing works

Deep Learning and Its Applications to Signal and Information Processing

Yu, D., & Deng, L. (2011) · IEEE Signal Processing Magazine · Cited by 159 · see citing works

Face Detection Using Convolutional Neural Networks and Gabor Filters

Kwolek, B. (2005) · Lecture notes in computer science · doi:10.1007/11550822_86 · Cited by 114 · see citing works

Perfusion magnetic resonance imaging of the liver

Thng, C. H. (2010) · World Journal of Gastroenterology · doi:10.3748/wjg.v16.i13.1598 · Cited by 112 · see citing works

Quantitative contrast enhanced ultrasound of the liver for time intensity curves—Reliability and potential sources of errors

Ignee, A., Jedrejczyk, M., Schuessler, G., Jakubowski, W. S., & Dietrich, C. F. (2009) · European Journal of Radiology · doi:10.1016/j.ejrad.2008.10.016 · Cited by 111 · see citing works

Contrast-Enhanced Sonography Helps in Discrimination of Benign From Malignant Adnexal Masses

Marret, H., Sauget, S., Giraudeau, B., Brewer, M., Ranger‐Moore, J., Body, G., & Tranquart, F. (2004) · Journal of Ultrasound in Medicine · doi:10.7863/jum.2004.23.12.1629 · Cited by 106 · see citing works

Staging and diagnosis of non‐small cell lung cancer: Invasive modalities

Yasufuku, K., & Fujisawa, T. (2007) · Respirology · doi:10.1111/j.1440-1843.2007.01035.x · Cited by 88 · see citing works

Focal liver lesions: contrast-enhanced ultrasound

Bartolotta, T. V., Taibbi, A., Midiri, M., & Lagalla, R. (2008) · Abdominal Imaging · doi:10.1007/s00261-008-9378-6 · Cited by 84 · see citing works

Parametric imaging for characterizing focal liver lesions in contrast-enhanced ultrasound

Rognin, N. G., Arditi, M., Mercier, L., Frinking, P. J. A., Schneider, M., Perrenoud, G., Anaye, A., Meuwly, J., & Tranquart, F. (2010) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2010.1716 · Cited by 81 · see citing works

Computer‐aided diagnosis for the classification of focal liver lesions by use of contrast‐enhanced ultrasonography

Shiraishi, J., Sugimoto, K., Moriyasu, F., Kamiyama, N., & Doi, K. (2008) · Medical Physics · doi:10.1118/1.2900109 · Cited by 71 · see citing works

A Scalable Formulation of Probabilistic Linear Discriminant Analysis: Applied to Face Recognition

Shafey, L. E., McCool, C., Wallace, R., & Marcel, S. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.38 · Cited by 71 · see citing works

Contrast-enhanced ultrasonography parameters in neural network diagnosis of liver tumors

Streba, C. T. (2012) · World Journal of Gastroenterology · doi:10.3748/wjg.v18.i32.4427 · Cited by 66 · see citing works

A data mining approach for fall detection by using k -nearest neighbour algorithm on wireless sensor network data

Erdoğan, Ş. Z., & Bilgin, T. T. (2012) · IET Communications · doi:10.1049/iet-com.2011.0228 · Cited by 64 · see citing works

Fast Classification of Meat Spoilage Markers Using Nanostructured ZnO Thin Films and Unsupervised Feature Learning

Längkvist, M., Coradeschi, S., Loutfi, A., & Rayappan, J. B. B. (2013) · Sensors · doi:10.3390/s130201578 · Cited by 52 · see citing works

Contrast-enhanced ultrasound for diagnosis of prostate cancer and kidney lesions

Mitterberger, M., Pelzer, A. E., Colleselli, D., Bartsch, G. H., Strasser, H., Pallwein, L., Aigner, F., Gradl, J., & Frauscher, F. (2007) · European Journal of Radiology · doi:10.1016/j.ejrad.2007.07.027 · Cited by 52 · see citing works

A posteriorirespiratory gating in contrast ultrasound for assessment of hepatic perfusion

Renault, G., Tranquart, F., Perlbarg, V., Bleuzen, A., Herment, A., & Frouin, F. (2005) · Physics in Medicine and Biology · doi:10.1088/0031-9155/50/19/003 · Cited by 48 · see citing works

Convolutional networks for speech detection

Sukittanon, S., Surendran, A. C., Platt, J. C., & Burges, C. J. C. (2004) · Interspeech (USB) · doi:10.21437/interspeech.2004-376 · Cited by 45 · see citing works

Software-based quantification of contrast-enhanced ultrasound in focal liver lesions—A feasibility study

Goertz, R. S., Bernatik, T., Strobel, D. D., Hahn, E. G., & Haendl, T. (2009) · European Journal of Radiology · doi:10.1016/j.ejrad.2009.11.004 · Cited by 31 · see citing works

Contrast-enhanced ultrasound for detection and diagnosis of renal clear cell carcinoma.

Dong, X., Shen, Y., Xu, L., Chun-mei, X., Bi, W., & Wang, X. (2009) · PubMed · Cited by 31 · see citing works

Respiratory motion correction in free-breathing ultrasound image sequence for quantification of hepatic perfusion.

Zhang, J., Ding, M.-Y., Meng, F., Yuchi, M., & Zhang, X.-M. (2011) · Medical Physics (Lancaster) · doi:10.1118/1.3606456 · Cited by 27 · see citing works

Hepatic Perfusion Imaging Using Factor Analysis of Contrast Enhanced Ultrasound

Lueck, G., Kim, T. K., Burns, P. N., & Martel, A. L. (2008) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2008.922695 · Cited by 26 · see citing works

Quantification of enhancement of focal liver lesions during contrast-enhanced ultrasound (CEUS). Analysis of ten selected frames is more simple but as reliable as the analysis of the entire loop for most parameters

Salvatore, V., Borghi, A., Sagrini, E., Galassi, M., Gianstefani, A., Bolondi, L., & Piscaglia, F. (2011) · European Journal of Radiology · doi:10.1016/j.ejrad.2011.01.097 · Cited by 22 · see citing works

Automating tumor classification with pixel-by-pixel contrast-enhanced ultrasound perfusion kinetics

Ta, C., Kono, Y., Barback, C. V., Mattrey, R. F., & Kummel, A. C. (2012) · Journal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena · doi:10.1116/1.3692962 · Cited by 17 · see citing works

Influence of radionuclide adsorption on detection efficiency and energy resolution for flow-cell radiation detectors

DeVol, T. A., Keillor, M. E., & Burggraf, L. W. (1996) · IEEE Transactions on Nuclear Science · doi:10.1109/23.507056 · Cited by 17 · see citing works

Quantitative Evaluation of Two-Factor Analysis Applied to Hepatic Perfusion Study Using Contrast-enhanced Ultrasound

Zhang, J., Ding, M., Meng, F., & Zhang, X. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2171038 · Cited by 16 · see citing works

Contrast‐Enhanced Doppler Perfusion Index

Ramnarine, K. V., Leen, E. L. S., Oppo, K., Angerson, W. J., & McArdle, C. S. (2002) · Journal of Ultrasound in Medicine · doi:10.7863/jum.2002.21.10.1121 · Cited by 15 · see citing works

Liver magnetic resonance imaging: State of the art

Sijens, P. E. (2010) · World Journal of Gastroenterology · doi:10.3748/wjg.v16.i13.1558 · Cited by 7 · see citing works

Sparse non-negative matrix factorizations for ultrasound factor analysis

Chen, X., Wu, K., Ding, M., & Sang, N. (2013) · Optik · doi:10.1016/j.ijleo.2013.04.048 · Cited by 3 · see citing works

Deep Learning in Medical Ultrasound Analysis: A Review

Liu, S., Wang, Y., Yang, X., Lei, B., Liu, L., Li, S. X., Ni, D., & Wang, T. (2019) · Engineering · doi:10.1016/j.eng.2018.11.020 · Cited by 798 · see citing works

Deep learning-based breast cancer classification through medical imaging modalities: State of the art and research challenges

Murtaza, G., Shuib, L., Abdul Wahab, A. W., Mujtaba, G., Mujtaba, G., Nweke, H. F., Al-garadi, M. A., Zulfiqar, F., Raza, G., & Azmi, N. A. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-019-09716-5 · Cited by 342 · see citing works

A Survey of Deep-Learning Applications in Ultrasound: Artificial Intelligence–Powered Ultrasound for Improving Clinical Workflow

Akkus, Z., Cai, J. C., Boonrod, A., Zeinoddini, A., Weston, A. D., Philbrick, K. A., & Erickson, B. J. (2019) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2019.06.004 · Cited by 324 · see citing works

Machine learning for medical ultrasound: status, methods, and future opportunities

Brattain, L. J., Telfer, B. A., Dhyani, M., Grajo, J. R., & Samir, A. E. (2018) · Abdominal Radiology · doi:10.1007/s00261-018-1517-0 · Cited by 262 · see citing works

Deep learning based liver cancer detection using watershed transform and Gaussian mixture model techniques

Das, A., Acharya, U. R., Panda, S. S., & Sabut, S. (2018) · Cognitive Systems Research · doi:10.1016/j.cogsys.2018.12.009 · Cited by 200 · see citing works

Stacked deep polynomial network based representation learning for tumor classification with small ultrasound image dataset

Shi, J., Zhou, S., Liu, X., Zhang, Q., Lu, M., & Wang, T. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.01.074 · Cited by 166 · see citing works

Deep Learning in Medical Ultrasound Image Analysis: A Review

Wang, Y., Ge, X., Ma, H., Qi, S., Zhang, G., & Yao, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3071301 · Cited by 162 · see citing works

Deep Learning Radiomics Based on Contrast-Enhanced Ultrasound Might Optimize Curative Treatments for Very-Early or Early-Stage Hepatocellular Carcinoma Patients

Liu, F., Liu, D., Wang, K., Xie, X., Su, L., Kuang, M., Huang, G., Peng, B., Wang, Y., Lin, M., et al. (2020) · Liver Cancer · doi:10.1159/000505694 · Cited by 144 · see citing works

Liver Cancer Detection Using Hybridized Fully Convolutional Neural Network Based on Deep Learning Framework

Dong, X., Zhou, Y., Wang, L., Peng, J., Lou, Y., & Fan, Y. (2020) · IEEE Access · doi:10.1109/access.2020.3006362 · Cited by 131 · see citing works

Diagnosis of Focal Liver Diseases Based on Deep Learning Technique for Ultrasound Images

Hassan, T. M., Elmogy, M. M., & Sallam, E. A. (2017) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-016-2387-9 · Cited by 123 · see citing works

A Novel Method for Classifying Liver and Brain Tumors Using Convolutional Neural Networks, Discrete Wavelet Transform and Long Short-Term Memory Networks

Kutlu, H., & Avcı, E. (2019) · Sensors · doi:10.3390/s19091992 · Cited by 120 · see citing works

Preoperative classification of primary and metastatic liver cancer via machine learning-based ultrasound radiomics

Mao, B., Ma, J., Duan, S., Xia, Y., Tao, Y., & Zhang, L. (2021) · European Radiology · doi:10.1007/s00330-020-07562-6 · Cited by 115 · see citing works

A two-stage multi-view learning framework based computer-aided diagnosis of liver tumors with contrast enhanced ultrasound images

Guo, L., Wang, D., Qian, Y., Xiao, Z., Zhao, C.-K., Li, X., Bo, X., Yue, W., Zhang, Q., Shi, J., et al. (2018) · Clinical Hemorheology and Microcirculation · doi:10.3233/ch-170275 · Cited by 112 · see citing works

Computer-Aided Diagnosis of Focal Liver Lesions Using Contrast-Enhanced Ultrasonography With Perflubutane Microbubbles

Kondo, S., Takagi, K., Nishida, M., Iwai, T., Kudo, Y., Ogawa, K., Kamiyama, T., Shibuya, H., Kahata, K., & Shimizu, C. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2659734 · Cited by 91 · see citing works

A Novel Liver Image Classification Method Using Perceptual Hash-Based Convolutional Neural Network

Özyurt, F., Tuncer, T., Avcı, E., Koç, M., & Serhatlioğlu, İ. (2018) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-018-3454-1 · Cited by 87 · see citing works

Machine learning based liver disease diagnosis: A systematic review

Khan, R. A., Luo, Y., & Wu, F. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.08.138 · Cited by 92 · see citing works

Advances of AI in image-based computer-aided diagnosis: A review

Yeasmin, M. N., Amin, M. A., Joti, T. J., Aung, Z., & Azim, M. A. (2024) · Array · doi:10.1016/j.array.2024.100357 · Cited by 97 · see citing works

A Deep Learning Based Alternative to Beamforming Ultrasound Images

Nair, A. A., Tran, T. D., Reiter, A., & Bell, M. A. L. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8461575 · Cited by 79 · see citing works

The extraction method of tooth preparation margin line based on S‐Octree CNN

Zhang, B., Dai, N., Tian, S., Yuan, F., & Yu, Q. (2019) · International Journal for Numerical Methods in Biomedical Engineering · doi:10.1002/cnm.3241 · Cited by 66 · see citing works

A Novel Bio-Inspired Deep Learning Approach for Liver Cancer Diagnosis

Ghoniem, R. M. (2020) · Information · doi:10.3390/info11020080 · Cited by 70 · see citing works

Deep Neural Architectures for Contrast Enhanced Ultrasound (CEUS) Focal Liver Lesions Automated Diagnosis

Căleanu, C. D., Sîrbu, C. L., & Simion, G. (2021) · Sensors · doi:10.3390/s21124126 · Cited by 63 · see citing works

Deep learning for image-based liver analysis — A comprehensive review focusing on malignant lesions

Survarachakan, S., Prasad, P. J. R., Naseem, R., de Frutos, J. P., Kumar, R. P., Langø, T., Cheikh, F. A., Elle, O. J., & Lindseth, F. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102331 · Cited by 62 · see citing works

Focal liver lesion diagnosis with deep learning and multistage CT imaging

Wei, Y., Yang, M., Zhang, M., Gao, F., Zhang, N., Hu, F., Zhang, X., Zhang, S., Huang, Z., Xu, L., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-51260-6 · Cited by 60 · see citing works

Classification of focal liver lesions on ultrasound images by extracting hybrid textural features and using an artificial neural network

Hwang, Y. N., Lee, J. H., Kim, G. Y., Jiang, Y., & Kim, S. M. (2015) · Bio-Medical Materials and Engineering · doi:10.3233/bme-151459 · Cited by 60 · see citing works

Focal Liver Lesions: Computer-aided Diagnosis by Using Contrast-enhanced US Cine Recordings

Ta, C., Kono, Y., Eghtedari, M., Oh, Y. T., Robbin, M. L., Barr, R. G., Kummel, A. C., & Mattrey, R. F. (2017) · Radiology · doi:10.1148/radiol.2017170365 · Cited by 58 · see citing works

A survey on deep learning in medical ultrasound imaging

Song, K., Feng, J., & Chen, D. (2024) · Frontiers in Physics · doi:10.3389/fphy.2024.1398393 · Cited by 59 · see citing works

Deep Convolutional Neural Network-Based Automatic Classification of Neonatal Hip Ultrasound Images: A Novel Data Augmentation Approach with Speckle Noise Reduction

Sezer, A., & Sezer, H. B. (2019) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2019.09.018 · Cited by 57 · see citing works

Contrast-Enhanced Ultrasound Quantification: From Kinetic Modeling to Machine Learning

Turco, S., Frinking, P. J. A., Wildeboer, R. R., Arditi, M., Wijkstra, H., Lindner, J. R., & Mischi, M. (2020) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2019.11.008 · Cited by 55 · see citing works

A multi-modal deep neural network for multi-class liver cancer diagnosis

Khan, R. A., Fu, M., Burbridge, B., Luo, Y., & Wu, F. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.06.013 · Cited by 54 · see citing works

Multi-Source Transfer Learning Via Multi-Kernel Support Vector Machine Plus for B-Mode Ultrasound-Based Computer-Aided Diagnosis of Liver Cancers

Zhang, H., Guo, L., Wang, D., Wang, J., Bao, L., Ying, S., Xu, H., & Shi, J. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3073812 · Cited by 54 · see citing works

A new automated quantification algorithm for the detection and evaluation of focal liver lesions with contrast‐enhanced ultrasound

Gatos, I., Tsantis, S., Spiliοpoulos, S., Skouroliakou, A., Theotokas, I., Zoumpoulis, P. S., Hazle, J. D., & Kagadis, G. C. (2015) · Medical Physics · doi:10.1118/1.4921753 · Cited by 53 · see citing works

Deep learning for differentiation of benign and malignant solid liver lesions on ultrasonography

Xi, I. L., Wu, J., Guan, J., Zhang, P. J., Horii, S. C., Soulen, M. C., Zhang, Z., & Bai, H. X. (2020) · Abdominal Radiology · doi:10.1007/s00261-020-02564-w · Cited by 52 · see citing works

Interpretable Machine Learning for Characterization of Focal Liver Lesions by Contrast-Enhanced Ultrasound

Turco, S., Tiyarattanachai, T., Ebrahimkheil, K., Eisenbrey, J. R., Kamaya, A., Mischi, M., Lyshchik, A. P., & Kaffas, A. E. (2022) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2022.3161719 · Cited by 48 · see citing works

Radiological Diagnosis of Chronic Liver Disease and Hepatocellular Carcinoma: A Review

Singh, S., Hoque, S., Zekry, A., & Sowmya, A. (2023) · Journal of Medical Systems · doi:10.1007/s10916-023-01968-7 · Cited by 53 · see citing works

Hybrid classification of diffuse liver diseases in ultrasound images using deep convolutional neural networks

Pasyar, P., Mahmoudi, T., Kouzehkanan, S.-Z. M., Ahmadian, A., Arabalibeik, H., Soltanian, N., & Radmard, A. R. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100496 · Cited by 45 · see citing works

Artificial intelligence in liver ultrasound

Cao, L.-L., Peng, M., Xie, X. T., Chen, G.-Q., Huang, S.-Y., Wang, J., Jiang, F., Cui, X., & Dietrich, C. F. (2022) · World Journal of Gastroenterology · doi:10.3748/wjg.v28.i27.3398 · Cited by 41 · see citing works

Deep Learning Assisted Detection of Abdominal Free Fluid in Morison's Pouch During Focused Assessment With Sonography in Trauma

Cheng, C.-Y., Chiu, I.-M., Hsu, M.-Y., Pan, H.-Y., Tsai, C., & Lin, C. R. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.707437 · Cited by 39 · see citing works

Hierarchical Temporal Attention Network for Thyroid Nodule Recognition Using Dynamic CEUS Imaging

Wan, P., Chen, F., Liu, C., Kong, W., & Zhang, D. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3063421 · Cited by 39 · see citing works

Joint Segmentation and Differential Diagnosis of Thyroid Nodule in Contrast-Enhanced Ultrasound Images

Chen, F., Han, H.-J., Wan, P., Liao, H., Liu, C.-R., & Zhang, D. (2023) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2023.3262842 · Cited by 37 · see citing works

CEUS-based classification of liver tumors with deep canonical correlation analysis and multi-kernel learning

Guo, L., Wang, D., Xu, H., Qian, Y., Wang, C., Xiao, Z., Zhang, Q., & Shi, J. (2017) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2017.8037181 · Cited by 35 · see citing works

Explainable deep learning algorithm for distinguishing incomplete Kawasaki disease by coronary artery lesions on echocardiographic imaging

Lee, H., Eun, Y., Hwang, J. Y., & Eun, L. Y. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106970 · Cited by 35 · see citing works

Efficient Local Cloud-Based Solution for Liver Cancer Detection Using Deep Learning

Anil, B. C., Dayananda, P., Nethravathi, B., & Raisinghani, M. S. (2021) · International Journal of Cloud Applications and Computing · doi:10.4018/ijcac.2022010109 · Cited by 34 · see citing works

The effect of neural network architecture on virtual H&E staining: Systematic assessment of histological feasibility

Khan, U., Koivukoski, S., Valkonen, M., Latonen, L., & Ruusuvuori, P. (2023) · Patterns · doi:10.1016/j.patter.2023.100725 · Cited by 33 · see citing works

ANFIS based decision support system for prenatal detection of Truncus Arteriosus congenital heart defect

Sridevi, S., & Nirmala, S. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2015.09.002 · Cited by 33 · see citing works

The Potential of the Intel (R) Xeon Phi for Supervised Deep Learning

Viebke, A., & Sabri Pllana, A. (2015) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-css-icess.2015.45 · Cited by 32 · see citing works

Diagnosis of hepatocellular carcinoma using deep network with multi-view enhanced patterns mined in contrast-enhanced ultrasound data

Feng, X., Cai, W., Zheng, R., Tang, L., Zhou, J., Wang, H., Liao, J., Luo, B., Cheng, W., An, W., et al. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105635 · Cited by 32 · see citing works

Multi-View Feature Transformation Based SVM+ for Computer-Aided Diagnosis of Liver Cancers With Ultrasound Images

Zhang, H., Guo, L., Wang, J., Ying, S., & Shi, J. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3233717 · Cited by 32 · see citing works

Feature Fusion for Diagnosis of Atypical Hepatocellular Carcinoma in Contrast- Enhanced Ultrasound

Zhou, J., Pan, F., Li, W., Hu, H., Wang, W., & Huang, Q. (2021) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2021.3110590 · Cited by 31 · see citing works

Artificial Intelligence in Hepatology- Ready for the Primetime

Kalapala, R., Rughwani, H., & Reddy, D. N. (2022) · Journal of Clinical and Experimental Hepatology · doi:10.1016/j.jceh.2022.06.009 · Cited by 28 · see citing works

A review of medical image detection for cancers in digestive system based on artificial intelligence

Xu, J., Jing, M., Wang, S., Yang, C., & Chen, X. (2019) · Expert Review of Medical Devices · doi:10.1080/17434440.2019.1669447 · Cited by 28 · see citing works

Machine Learning–Aided Chronic Kidney Disease Diagnosis Based on Ultrasound Imaging Integrated with Computer-Extracted Measurable Features

Lee, S., Kang, M., Byeon, K., Lee, S. E., Lee, I. H., Kim, Y. A., Kang, S., & Park, J. T. (2022) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-022-00625-8 · Cited by 28 · see citing works

CHAOS: a parallelization scheme for training convolutional neural networks on Intel Xeon Phi

Viebke, A., Memeti, S., Sabri Pllana, A., & Abraham, A. (2017) · The Journal of Supercomputing · doi:10.1007/s11227-017-1994-x · Cited by 24 · see citing works

Deep learning radiomics for focal liver lesions diagnosis on long-range contrast-enhanced ultrasound and clinical factors

Liu, L., Tang, C., Li, L., Chen, P., Tan, Y. C., Hu, X., Chen, K., Shang, Y., Deng, L., Liu, H., et al. (2022) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-21-1004 · Cited by 24 · see citing works

A Hybrid Model of Faster R-CNN and SVM for Tumor Detection and Classification of MRI Brain Images

al., M. O. K. E. A. M. O. K. E., & TJPRC (2020) · International journal of mechanical and production engineering research and development · doi:10.24247/ijmperdjun2020651 · Cited by 24 · see citing works

Malignancy diagnosis of liver lesion in contrast enhanced ultrasound using an end-to-end method based on deep learning

Zhou, H., Ding, J., Zhou, Y., Wang, Y., Zhao, L., Shih, C.-C., Xu, J., Wang, J., Tong, L., Chen, Z., et al. (2024) · BMC Medical Imaging · doi:10.1186/s12880-024-01247-y · Cited by 23 · see citing works

An ultrasound image‐based deep multi‐scale texture network for liver fibrosis grading in patients with chronic HBV infection

Ruan, D., Shi, Y., Jin, L., Yang, Q., Yu, W., Ren, H., Zheng, W., Chen, Y., Zheng, N., & Zheng, M. (2021) · Liver International · doi:10.1111/liv.14999 · Cited by 23 · see citing works

Artificial Intelligence in the Diagnosis of Hepatocellular Carcinoma: A Systematic Review

Martinino, A., Aloulou, M., Chatterjee, S., Pereira, J. P. S., Singhal, S., Patel, T., Kirchgesner, T., Agnes, S., Annunziata, S., Treglia, G., et al. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11216368 · Cited by 25 · see citing works

Artificial intelligence in liver disease

Lee, H. W., Sung, J. J. �., & Ahn, S. H. (2021) · Journal of Gastroenterology and Hepatology · doi:10.1111/jgh.15409 · Cited by 23 · see citing works

Artificial intelligence techniques in liver cancer

Wang, L., Fatemi, M., & Alizad, A. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1415859 · Cited by 22 · see citing works

Explainable and Robust Deep Learning for Liver Segmentation Through U-Net Network

Brunese, M. C., Rocca, A., Santone, A., Cesarelli, M., Brunese, L., & Mercaldo, F. (2025) · Diagnostics · doi:10.3390/diagnostics15070878 · Cited by 21 · see citing works

The overview of the deep learning integrated into the medical imaging of liver: a review

Xiang, K., Jiang, B., & Shang, D. (2021) · Hepatology International · doi:10.1007/s12072-021-10229-z · Cited by 21 · see citing works

Application of Compressive Sensing to Ultrasound Images: A Review

Yousufi, M., Amir, M., Javed, U., Tayyib, M., Abdullah, S., Ullah, H., Qureshi, I. M., Alimgeer, K. S., Akram, M. W., & Khan, K. B. (2019) · BioMed Research International · doi:10.1155/2019/7861651 · Cited by 21 · see citing works

Architecture Optimization for Hybrid Deep Residual Networks in Liver Tumor Segmentation Using a GA

Reyad, M., Sarhan, A. M., & Arafa, M. M. (2024) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-024-00542-4 · Cited by 20 · see citing works

Artificial Intelligence for Contrast-Enhanced Ultrasound of the Liver: A Systematic Review

Brooks, J. A., Kallenbach, M., Radu, I.-P., Berzigotti, A., Dietrich, C. F., Kather, J. N., Luedde, T., & Seraphin, T. P. (2024) · Digestion · doi:10.1159/000541540 · Cited by 17 · see citing works

Automatic 3D Ultrasound Segmentation of Uterus Using Deep Learning

Behboodi, B., Rivaz, H. J., Lalondrelle, S., & Harris, E. (2021) · IEEE International Ultrasonics Symposium · doi:10.1109/ius52206.2021.9593671 · Cited by 16 · see citing works

Ultrasound tissue classification: a review

Shan, C., Tan, T., Han, J., & Huang, D. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09920-8 · Cited by 15 · see citing works

Diagnostic Performance of an Artificial Intelligence Model Based on Contrast-Enhanced Ultrasound in Patients with Liver Lesions: A Comparative Study with Clinicians

Urhuț, M. C., Săndulescu, L., Streba, C. T., Mămuleanu, M., Ciocâlteu, A., Cazacu, S. M., & Dănoiu, S. (2023) · Diagnostics · doi:10.3390/diagnostics13213387 · Cited by 15 · see citing works

Artificial intelligence for the classification of focal liver lesions in ultrasound – a systematic review

Vetter, M., Waldner, M. J., Zundler, S., Klett, D., Bocklitz, T. W., Neurath, M. F., Adler, W., & Jesper, D. (2023) · Ultraschall in der Medizin - European Journal of Ultrasound · doi:10.1055/a-2066-9372 · Cited by 14 · see citing works

Hepatocellular Carcinoma Segmentation within Ultrasound Images using Convolutional Neural Networks

Vancea, F. I., Mitrea, D. A., Nedevschi, S., Rotaru, M., Ştefănescu, H., & Badea, R. I. (2019) · IEEE International Conference on Intelligent Computer Communication and Processing (ICCP) · doi:10.1109/iccp48234.2019.8959687 · Cited by 14 · see citing works

Deep CNN for Contrast-Enhanced Ultrasound Focal Liver Lesions Diagnosis

Sîrbu, C. L., Simion, G., & Căleanu, C. D. (2020) · International Symposium on Electronics and Telecommunications (ISETC) · doi:10.1109/isetc50328.2020.9301116 · Cited by 14 · see citing works

Applying artificial intelligence to big data in hepatopancreatic and biliary surgery: a scoping review

McGivern, K., Drake, T. M., Knight, S. R., Lucocq, J. E., Bernabéu, M. O., Clark, N., Fairfield, C. J., Pius, R., Shaw, C. A., Seth, S., et al. (2023) · Artificial Intelligence Surgery · doi:10.20517/ais.2022.39 · Cited by 14 · see citing works

Ultrasound Medical Images Classification Based on Deep Learning Algorithms: A Review

Kareem, F. Q., & Abdulazeez, A. M. (2021) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.4621288 · Cited by 13 · see citing works

BUS-net: a bimodal ultrasound network for breast cancer diagnosis

Gong, X., Zhao, X. F., Fan, L., Li, T., Guo, Y., & Luo, J. (2022) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-022-01596-6 · Cited by 13 · see citing works

Ultrasound Medical Images Classification Based on Deep Learning Algorithms: A Review

admin, A., & Abdulazeez, A. M. (2021) · Fusion Practice and Applications · doi:10.54216/fpa.030102 · Cited by 13 · see citing works

Deep learning classification of focal liver lesions with contrast-enhanced ultrasound from arterial phase recordings

Kim, N., Lee, W. J., & Lee, H.-J. (2023) · International Conference on Electronics, Information, and Communication (ICEIC) · doi:10.1109/iceic57457.2023.10049872 · Cited by 11 · see citing works

Deep-Learning Model for Quality Assessment of Urinary Bladder Ultrasound Images Using Multiscale and Higher-Order Processing

Raina, D., Chandrashekhara, S. H., Voyles, R. M., Wachs, J. P., & Saha, S. K. (2024) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2024.3386919 · Cited by 11 · see citing works

A Multi-Scale and Multi-Level Fusion Approach for Deep Learning-Based Liver Lesion Diagnosis in Magnetic Resonance Images with Visual Explanation

Wan, Y., Zheng, Z., Liu, R., Zhu, Z., Zhou, H., Zhang, X., & Boumaraf, S. (2021) · Life · doi:10.3390/life11060582 · Cited by 11 · see citing works

B-mode ultrasound based CAD for liver cancers via multi-view privileged information learning

Han, X., Gong, B., Guo, L., Wang, J., Ying, S., Li, S., & Shi, J. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.03.028 · Cited by 10 · see citing works

Application of artificial intelligence-based computer vision methods in liver diseases: a bibliometric analysis

Feng, Y., Wang, Q., Su, Y., Ma, W., Du, G., Wu, J., Liu, J., & Wang, Y. (2025) · Intelligent Medicine · doi:10.1016/j.imed.2024.09.008 · Cited by 10 · see citing works

Multimodal Ultrasound imaging based diagnosis of liver cancers with a two-stage multi-view learning framework

Qian, Y., Shi, J., Xiao, Z., Zhang, Q., Guo, L., Wang, D., & Xu, H. (2017) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2017.8037545 · Cited by 10 · see citing works

Machine learning for malignant versus benign focal liver lesions on US and CEUS: a meta-analysis

Jorge, C. A. C., Castanha, E. B., Vilardo, M., Staziaki, P. V., Francisco, M. Z., Mohajer, B., Watte, G., de Moraes, F. Y., a, B. H., & Altmayer, S. (2023) · Abdominal Radiology · doi:10.1007/s00261-023-03984-0 · Cited by 9 · see citing works

Machine learning classification of speckle patterns for roughness measurements

Castilho, V. M., Balthazar, W. F., da Silva, L., Penna, T. J. P., & Huguenin, J. A. O. (2023) · Physics Letters A · doi:10.1016/j.physleta.2023.128736 · Cited by 9 · see citing works

Domain knowledge-guided adversarial adaptive fusion of hybrid breast ultrasound data

Gong, X., Yuan, S., Xiang, Y., Fan, L., & Zhou, H. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107256 · Cited by 9 · see citing works

Classification for Rectal CEUS Images Based on Combining Features by Transfer Learning

Qin, L., Yin, H., Zhuang, H., Luo, Y., Liu, P., & Liu, D. C. (2019) · International Symposium on Image Computing and Digital Medicine (ISICDM) · doi:10.1145/3364836.3364873 · Cited by 9 · see citing works

MUCM-FLLs: Multimodal ultrasound-based classification model for focal liver lesions

Li, T., Guo, J., Tao, W., Bu, R., & Feng, T. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107864 · Cited by 8 · see citing works

Research Status and Prospects of Deep Learning in Medical Images

Liang, C. C., & Xin, S. (2020) · International Conference on Communications, Information System and Computer Engineering (CISCE) · doi:10.1109/cisce50729.2020.00084 · Cited by 8 · see citing works

A Comparative Study on Liver Tumor Detection Using CT Images

Alawi, A. E. B., Saeed, A. Y. A., Radman, B. M. N., & Al-Zekri, B. T. (2021) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-030-70713-2_14 · Cited by 8 · see citing works

An Automated Method for Classifying Liver Lesions in Contrast-Enhanced Ultrasound Imaging Based on Deep Learning Algorithms

Mămuleanu, M., Urhuț, M. C., Săndulescu, L., Kamal, C., Pătrașcu, A.-M., Ionescu, A. G., Şerbănescu, M.-S., & Streba, C. T. (2023) · Diagnostics · doi:10.3390/diagnostics13061062 · Cited by 8 · see citing works

Analysis of neural networks for routine classification of sixteen ultrasound upper abdominal cross sections

Lawley, A., Hampson, R., Worrall, K., & Dobie, G. (2024) · Abdominal Radiology · doi:10.1007/s00261-023-04147-x · Cited by 8 · see citing works

Liver Segmentation and Liver Cancer Detection Based on Deep Convolutional Neural Network: A Brief Bibliometric Survey

Napte, K. M., & Mahajan, A. (2021) · Lincoln (University of Nebraska) · Cited by 8 · see citing works

FireNet-MLstm for classifying liver lesions by using deep features in CT images

Kabe, G. K., Song, Y., & Liu, Z. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11411-z · Cited by 8 · see citing works

Detail Study of Different Algorithms for Early Detection of Cancer

Dhar, P., Devi, K. S., Satti, S. K., & Srinivasan, P. (2021) · Studies in computational intelligence · doi:10.1007/978-981-15-9735-0_12 · Cited by 7 · see citing works

Tensor Flow Based Analysis and Classification of Liver Disorders from Ultrasonography Images

Krishnan, K., Midhila, M., & Sudhakar, R. (2018) · Lecture notes in computational vision and biomechanics · doi:10.1007/978-3-319-71767-8_64 · Cited by 7 · see citing works

Evaluation of Tracking Algorithms for Contrast Enhanced Ultrasound Imaging Exploration

Sîrbu, C. L., Seiculescu, C., Burdan, G. A., Moga, T. V., & Căleanu, C. D. (2022) · Australasian Computer Science Week Multiconference (ACSW) · doi:10.1145/3511616.3513111 · Cited by 7 · see citing works

Design of a Multi-Stage Ensemble Model for Thyroid Prediction Using Learning Approaches

Prasad, M. L. M., & Santhosh, R. (2024) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2023.036628 · Cited by 6 · see citing works

An Efficient and Accurate DDPG-Based Recurrent Attention Model for Object Localization

Ke, F. (2020) · IEEE Access · doi:10.1109/access.2020.3008171 · Cited by 6 · see citing works

Adversarial Neural Network Classifiers for COVID-19 Diagnosis in Ultrasound Images

Karar, M. E., Shouman, M. A., & Chalopin, C. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.018564 · Cited by 6 · see citing works

Comparative analysis of the DCNN and HFCNN Based Computerized detection of liver cancer

Pande, S. K., Kalyani, P., Nagendram, S., Alluhaidan, A. S., Gade, H. B., Ahammad, S. H., Pandey, V. K., Sridevi, G., Kumar, A., & Bonyah, E. (2025) · BMC Medical Imaging · doi:10.1186/s12880-025-01578-4 · Cited by 5 · see citing works

Classification of focal liver disease in egyptian patients using ultrasound images and convolutional neural networks

Abd-Elghaffar, R., El-Zalabany, M., Moustafa, H. E.-D., & El-Seddek, M. (2022) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v27.i2.pp793-802 · Cited by 5 · see citing works

An Automated Method for Classifying Liver Lesions in Contrast-Enhanced Ultrasound Imaging Based on Deep Learning Algorithms

Mămuleanu, M., Urhuț, M. C., Săndulescu, L., Kamal, C., Pătrașcu, A.-M., Ionescu, A. G., Şerbănescu, M.-S., & Streba, C. T. (2023) · Diagnostics · doi:10.3390/diagnostics13061062 · Cited by 8 · see citing works

Kernel sparse representation based classification of focal liver lesions with contrast-enhanced ultrasound

Li, D., Yakui, Z., & Jin, J. (2017) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc.2017.7969968 · Cited by 1 · see citing works

Malignancy diagnosis of liver lesion in contrast enhanced ultrasound using an end-to-end method based on deep learning

Zhou, H., Ding, J., Zhou, Y., Wang, Y., Zhao, L., Shih, C.-C., Xu, J., Wang, J., Tong, L., Chen, Z., et al. (2024) · BMC Medical Imaging · doi:10.1186/s12880-024-01247-y · Cited by 23 · see citing works

A unified end-to-end classification model for focal liver lesions

Zhao, L., Liu, S., An, Y., Cai, W., Li, B., Wang, S.-H., Liang, P., Yu, J., & Zhao, J. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105260 · Cited by 6 · see citing works

Deep Feature Extraction Using Channel Self-Attention for Focal Liver Lesion Classification

Lingamaiah, S. C., & Kokil, P. (2025) · IEEE Conference on Information and Communication Technology (CICT) · doi:10.1109/cict67193.2025.11398967