Avuçlu E. COVID-19 detection using X-ray images and statistical measurements. Measurement (Lond). 2022; 201: 111702. https://doi.org/10.1016/j.measurement.2022.111702

Cho HC, Sun S, Min Hyun C, Kwon JY, Kim B, Park Y, Seo JK. Automated ultrasound assessment of amniotic fluid index using deep learning. Med Image Anal. 2021; 69: 101951. https://doi.org/10.1016/j.media.2020.101951

Mento F, Soldati G, Prediletto R, Demi M, Demi L. Quantitative Lung Ultrasound Spectroscopy Applied to the Diagnosis of Pulmonary Fibrosis: The First Clinical Study. IEEE Trans Ultrason Ferroelectr Freq Control. 2020; 67 (11): 2265-2273. https://doi.org/10.1109/TUFFC.2020.3012289

Huang Q, Lei Y, Xing W, He C, Wei G, Miao Z, Hao Y, Li G, Wang Y, Li Q, Li X, Li W, Chen J. Evaluation of Pulmonary Edema Using Ultrasound Imaging in Patients With COVID-19 Pneumonia Based on a Non-local Channel Attention ResNet. . Ultrasound Med Biol. 2022; 48 (5): 945-953. https://doi.org/10.1016/j.ultrasmedbio.2022.01.023

Wang L, Zhang L, Zhu M, Qi X, Yi Z. Automatic diagnosis for thyroid nodules in ultrasound images by deep neural networks. Med Image Anal. 2020; 61: 101665. https://doi.org/10.1016/j.media.2020.101665

Zhang Y, Su L, Liu Z, Tan W, Jiang Y, Cheng C. A. Semi-supervised learning approach for COVID-19 detection from chest CT scans. Neurocomputing (Amst). 2022; 503: 314-324. https://doi.org/10.1016/j.neucom.2022.06.076

Velichko E, Shariaty F, Orooji M, Pavlov V, Pervunina T, Zavjalov S, Khazaei R, Radmard AR. Development of computer-aided model to differentiate COVID-19 from pulmonary edema in lung CT scan: EDECOVID-net. Comput Biol Med. 2022; 141: 105172. https://doi.org/10.1016/j.compbiomed.2021.105172

Imani M. Automatic diagnosis of coronavirus (COVID-19) using shape and texture characteristics extracted from X-Ray and CT-Scan images. Biomed Signal Process Control. 2021; 68: 102602. https://doi.org/10.1016/j.bspc.2021.102602

Kuo CC, Chang CM, Liu KT, Lin WK, Chiang HY, Chung CW, Ho MR, Sun PR, Yang RL, Chen KT. Automation of the kidney function prediction and classification through ultrasound-based kidney imaging using deep learning. NPJ Digit Med. 2019; 26: 2:29. https://doi.org/10.1038/s41746-019-0104-2

Khan U, Afrakhteh S, Mento F, Mert G, Smargiassi A, Inchingolo R, Tursi F, Macioce VN, Perrone T, Iacca G, Demi L (2024) Low-complexity lung ultrasound video scoring by means of intensity projection-based video compression. Comput Biol Med. 169. https://doi.org/10.1016/j.compbiomed.2023.107885

Carrer L, Donini E, Marinelli D, Zanetti M, Mento F, Torri E, Smargiassi A, Inchingolo R, Soldati G, Demi L, Bovolo F, Bruzzone L. Automatic Pleural Line Extraction and COVID-19 Scoring From Lung Ultrasound Data. IEEE Trans Ultrason Ferroelectr Freq Control. 2020; 67 (11): 2207-2217. https://doi.org/10.1109/TUFFC.2020.3005512

Anantrasirichai N, Hayes W, Allinovi M, Bull D, Achim A. Line Detection as an Inverse Problem: Application to Lung Ultrasound Imaging. IEEE Trans Med Imaging. 2017; 36 (10): 2045-2056. https://doi.org/10.1109/TMI.2017.2715880

Xue W, Cao C, Liu J, Duan Y, Cao H, Wang J, Tao X, Chen Z, Wu M, Zhang J, Sun H, Jin Y, Yang X, Huang R, Xiang F, Song Y, You M, Zhang W, Jiang L, Zhang Z, Kong S, Tian Y, Zhang L, Ni D, Xie M. Modality alignment contrastive learning for severity assessment of COVID-19 from lung ultrasound and clinical information. Med Image Anal. 2021; 69: 101975. https://doi.org/10.1016/j.media.2021.101975

La Salvia M, Secco G, Torti E, Florimbi G, Guido L, Lago P, Salinaro F, Perlini S, Leporati F. Deep learning and lung ultrasound for Covid-19 pneumonia detection and severity classification. Comput Biol Med. 2021; 136: 104742. https://doi.org/10.1016/j.compbiomed.2021.104742

Roy S, Menapace W, Oei S, Luijten B, Fini E, Saltori C, Huijben I, Chennakeshava N, Mento F, Sentelli A, Peschiera E, Trevisan R, Maschietto G, Torri E, Inchingolo R, Smargiassi A, Soldati G, Rota P, Passerini A, van Sloun RJG, Ricci E, Demi L. Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound. IEEE Trans Med Imaging. 2020; 39 (8): 2676-2687. https://doi.org/10.1109/TMI.2020.2994459

Khan U, Mento F, Nicolussi Giacomaz L, Trevisan R, Smargiassi A, Inchingolo R, Perrone T, Demi L. Deep Learning-Based Classification of Reduced Lung Ultrasound Data From COVID-19 Patients. IEEE Trans Ultrason Ferroelectr Freq Control. 2022; 69 (5): 1661-1669. https://doi.org/10.1109/TUFFC.2022.3161716

Bruno A, Ignesti G, Salvetti O, Moroni D, Martinelli M. Efficient Lung Ultrasound Classification. Bioengineering (Basel). 2023; 10 (555): https://doi.org/10.3390/bioengineering10050555

Zhao G, Kong D, Xu X, Hu S, Li Z, Tian J. Deep learning-based classification of breast lesions using dynamic ultrasound video. Eur J Radiol. 2023; 165: 110885. https://doi.org/10.1016/j.ejrad.2023.110885

Arntfield R, Wu D, Tschirhart J, VanBerlo B, Ford A, Ho J, McCauley J, Wu B, Deglint J, Chaudhary R, Dave C, VanBerlo B, Basmaji J, Millington S. Automation of Lung Ultrasound Interpretation via Deep Learning for the Classification of Normal versus Abnormal Lung Parenchyma: A Multicenter Study. Diagnostics (Basel). 2021; 4 (11): https://doi.org/10.3390/diagnostics11112049

Dastider AG, Sadik F, Fattah SA. An integrated autoencoder-based hybrid CNN-LSTM model for COVID-19 severity prediction from lung ultrasound. Comput Biol Med. 2021; 132: 104296. https://doi.org/10.1016/j.compbiomed.2021.104296

Muhammad G, Shamim Hossain M. COVID-19 and Non-COVID-19 Classification using Multi-layers Fusion From Lung Ultrasound Images. Inf Fusion. 2021; 72: 80-88. https://doi.org/10.1016/j.inffus.2021.02.013

Khan U., Thompson R., Li,J., Etter,L., Camelo,I.Y., Pieciak,R.C., Castro-Aragon I., Setty B.R., Gill C.C., Demi L., Betke M. FLUEnT: Transformer for detecting lung consolidations in videos using fused lung ultrasound encodings. Computers in biology and medicine. 2024; 180: 109014 https://doi.org/10.1016/j.compbiomed.2024.109014

Wu X, Tan G, Luo H, Chen Z, Pu B, Li S, Li K. A knowledge-interpretable multi-task learning framework for automated thyroid nodule diagnosis in ultrasound videos. Med Image Anal. 2024; 91: 103039. https://doi.org/10.1016/j.media.2023.103039

Erfanian Ebadi S, Krishnaswamy D, Bolouri SES, Zonoobi D, Greiner R, Meuser-Herr N, Jaremko JL, Kapur J, Noga M, Punithakumar K. Automated detection of pneumonia in lung ultrasound using deep video classification for COVID-19. Inform Med Unlocked. 2021; 25: 100687. https://doi.org/10.1016/j.imu.2021.100687

Feng X, Chen X, Dong C, Liu Y, Liu Z, Ding R, Huang Q. Multi-scale information with attention integration for classification of liver fibrosis in B-mode US image. Comput Methods Programs Biomed. 2022; 215 (106598): https://doi.org/10.1016/j.cmpb.2021.106598

J Wang, MATLAB Deep Learning Model Hub, in, 2024.

Dheepak G, J AC, Vaishali D. Brain tumor classification: a novel approach integrating GLCM, LBP and composite features. Front Oncol. 2024; 13: 1248452. https://doi.org/10.3389/fonc.2023.1248452.

Yu R, Tian Y, Gao J, Liu Z, Wei X, Jiang H, Huang Y, Li X. Feature discretization-based deep clustering for thyroid ultrasound image feature extraction. Comput Biol Med. 2022; 146: 105600. https://doi.org/10.1016/j.compbiomed.2022.105600

Prasad G.N., Gaddale V.S., Kamath R.C., Shekaranaik V.J., Pai S.P. A Study of Dimensionality Reduction in GLCM Feature-Based Classification of Machined Surface Images. Arabian Journal for Science and Engineering. 2023; 1: 23. https://doi.org/10.1007/s13369-023-07854-1

Corrias G, Micheletti G, Barberini L, Suri JS, Saba L. Texture analysis imaging "what a clinical radiologist needs to know". Eur J Radiol. 2022; 146: 110055. https://doi.org/10.1016/j.ejrad.2021.110055

Haralick R. M. Statistical and structural approaches to texture. Proceedings of the IEEE. 1979; 67 (5): 786-804. https://doi.org/10.1109/PROC.1979.11328

Materka A, Strzelecki M. Texture Analysis Methods - A Review. COST B11 report. 1998:

G. Muhammad, M. S. Hossain, A. Yassine. Tree-Based Deep Networks for Edge Devices. Ieee T Ind Inform. 2020; 16 (3): 2022-2028. https://doi.org/10.1109/Tii.2019.2950326

Demi L, Wolfram F, Klersy C, De Silvestri A, Ferretti VV, Muller M, Miller D, Feletti F, Wełnicki M, Buda N, Skoczylas A, Pomiecko A, Damjanovic D, Olszewski R, Kirkpatrick AW, Breitkreutz R, Mathis G, Soldati G, Smargiassi A, Inchingolo R, Perrone T. New International Guidelines and Consensus on the Use of Lung Ultrasound. J Ultrasound Med. 2023; 42 (2): 309-344. https://doi.org/10.1002/jum.16088

Roy S, Menapace W, Oei S, Luijten B, Fini E, Saltori C, Huijben I, Chennakeshava N, Mento F, Sentelli A, Peschiera E, Trevisan R, Maschietto G, Torri E, Inchingolo R, Smargiassi A, Soldati G, Rota P, Passerini A, van Sloun RJG, Ricci E, Demi L. Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound. IEEE Trans Med Imaging. 2020; 39 (8): 2676-2687.

Zhen C, Wang H, Cheng J, Yang X, Chen C, Hu X, Zhang Y, Cao Y, Ni D, Huang W, Wang P. Locating Multiple Standard Planes in First-Trimester Ultrasound Videos via the Detection and Scoring of Key Anatomical Structures. Ultrasound Med Biol. 2023; 49 (9): 2006-2016. https://doi.org/10.1016/j.ultrasmedbio.2023.05.005

Wang Y, Zhang Y, He Q, Liao H, Luo J. . Quantitative Analysis of Pleural Line and B-Lines in Lung Ultrasound Images for Severity Assessment of COVID-19 Pneumonia. IEEE Trans Ultrason Ferroelectr Freq Control. 2022; 69 (1): 73-83. https://doi.org/10.1109/TUFFC.2021.3107598

Materka A. Texture analysis methodologies for magnetic resonance imaging. Dialogues Clin Neurosci. 2004; 6 (2): 243–250. https://doi.org/10.31887/DCNS.2004.6.2/amaterka

Seoni S, Matrone G, Meiburger KM. . Texture analysis of ultrasound images obtained with different beamforming techniques and dynamic ranges - A robustness study. Ultrasonics. 2023; 131: 106940. https://doi.org/10.1016/j.ultras.2023.106940

Wu CM, Chen YC, Hsieh KS. Texture features for classification of ultrasonic liver images. IEEE Trans Med Imaging. 1992; 11 (2): 141-152. https://doi.org/10.1109/42.141636

Zhao X, Shen X, Wan W, Lu Y, Hu S, Xiao R, Du X, Li J. Automatic Thyroid Ultrasound Image Classification Using Feature Fusion Network. IEEE Access. 2022; 10: 27917-27924. https://doi.org/10.1109/ACCESS.2022.3156096

Manisha V, Balasubramanian R, Subrahmanyam M. Local extrema co-occurrence pattern for color and texture image retrieval. Neurocomputing,. 2015: 255–269. https://doi.org/10.1016/j.neucom.2015.03.015.

Hathaway QA, Yanamala N, Siva NK, Adjeroh DA, Hollander JM, Sengupta PP. Ultrasonic Texture Features for Assessing Cardiac Remodeling and Dysfunction. J Am Coll Cardiol. 2022; 80 (23): 2187–2201. https://doi.org/10.1016/j.jacc.2022.09.036

Szegedy C., Ioffe S., Vanhouck, V., Alemi A. Inception-v4, inception-resnet and the impact of residual connections on learning. In Proceedings of the AAAI conference on artificial intelligence. 2017; 31 (1): https://doi.org/10.48550/arXiv.1602.07261

Chollet F. Xception: Deep Learning with Depthwise Separable Convolutions. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016: 1800–1807. https://doi.org/10.48550/arXiv.1610.02357

Su C, Zhou Y, Ma J, Chi H, Jing X, Jiao J, Yan Q. JANet: A joint attention network for balancing accuracy and speed in left ventricular ultrasound video segmentation. Comput Biol Med. 2024; 169: 107856. https://doi.org/10.1016/j.compbiomed.2023.107856

Tao X, Cao Y, Jiang Y, Wu X, Yan D, Xue W, Zhuang S, Yang X, Huang R, Zhang J, Ni D. Enhancing lesion detection in automated breast ultrasound using unsupervised multi-view contrastive learning with 3D DETR. Med Image Anal. 2025; 2025: 103466. https://doi.org/10.1016/j.media.2025.103466

Wiedemann N, Boer D.d.K, Richte M, Weijer Svd, Buhre C, Eggert F.A.M. COVID-BLUeS - A Prospective Study on the Value of AI in Lung Ultrasound Analysis. IEEE Journal of Biomedical and Health Informatics. 2025: 1 - 12. https://doi.org/10.1109/JBHI.2025.3543686

Soldati G, Smargiassi A, Inchingolo R, Buonsenso D, Perrone T, Briganti DF, Perlini S, Torri E, Mariani A, Mossolani EE, Tursi F, Mento F, Demi L. Proposal for International Standardization of the Use of Lung Ultrasound for Patients With COVID-19: A Simple, Quantitative, Reproducible Method. J Ultrasound Med. 2020; 39 (7): 1413-1419. https://doi.org/10.1002/jum.15285

J. Born, , Wiedemann, N., Cossio, M., Buhre, C., Brändle, G., Leidermann, K., Goulet, J., Aujayeb, A., Moor, M., Rieck, B., Borgwardt, K. Accelerating Detection of Lung Pathologies with Explainable Ultrasound Image Analysis. Applied Sciences. 2021; 11 (2): 672. https://doi.org/10.3390/app11020672

Khan U, Afrakhteh S, Mento F, Fatima N, Rosa LD, Custode LL, Azam Z, Torri E, Soldati G, Tursi F, Macioce VN, Smargiassi A, Inchingolo R, Perrone T, Iacca G, Demi L. Benchmark methodological approach for the application of artificial intelligence to lung ultrasound data from COVID-19 patients: From frame to prognostic-level. 2023; 132: 106994. https://doi.org/10.1016/j.ultras.2023.106994

Cheng J, Tian S, Yu L, Gao C, Kang X, Ma X, Wu W, Liu S, Lu H. . ResGANet: Residual group attention network for medical image classification and segmentation. Med Image Anal. . 2022; 76: 102313. https://doi.org/10.1016/j.media.2021.102313

Ebadi A, Xi P, MacLean A, Florea A, Tremblay S, Kohli S, Wong A. COVIDx-US: An Open-Access Benchmark Dataset of Ultrasound Imaging Data for AI-Driven COVID-19 Analytics. Front Biosci (Landmark Ed). 2022; 27 (7): 198. https://doi.org/10.31083/j.fbl2707198

Zhao L., Fong T.C. & Bell M.A.L. Detection of COVID-19 features in lung ultrasound images using deep neural networks. Commun Med. 2024; 4: 41. https://doi.org/10.1038/s43856-024-00463-5

Q. Wang, T. J. Zou, X. Y. Zeng, T. Bao, W. H. Yin. Establishment of seven lung ultrasound phenotypes: a retrospective observational study of an LUS registry. Bmc Pulm Med. 2024; 24 (1): https://doi.org/10.1186/s12890-024-03299-w

G. H. Wei, H. Ma, W. Qian, F. F. Han, H. Y. Jiang, S. L. Qi, M. Qiu. Lung nodule classification using local kernel regression models with out-of-sample extension. Biomed Signal Proces. 2018; 40: 1-9. https://doi.org/10.1016/j.bspc.2017.08.026