{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T17:22:07Z","timestamp":1788456127526,"version":"build-2803163510"},"reference-count":38,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Early and accurate detection of liver masses is essential for effective clinical management and improved patient outcomes, as treatment strategies differ significantly between benign and malignant lesions. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is widely used for liver lesion evaluation due to its ability to capture temporal enhancement behavior. However, reliable interpretation remains challenging because many lesions exhibit similar enhancement patterns, leading to diagnostic uncertainty and potential misclassification. This study proposes a hybrid diagnostic framework that integrates multiple complementary feature representations for automated liver mass classification. Specifically, the model extracts time\u2013intensity curve (TIC) characteristics to capture contrast dynamics, deep learning features to represent complex spatial patterns, and fractional Zernike\u2013Caputo descriptors to encode advanced mathematical shape and texture information. These heterogeneous feature sets are subsequently fused to form a unified and discriminative representation of each lesion. The proposed study aims to enhance the differentiation between benign and malignant liver masses by leveraging the strengths of kinetic, data-driven, and fractional mathematical descriptors. Experimental results demonstrate that the model achieves superior diagnostic performance, reaching an accuracy of 94.05% on a dataset comprising 645 dynamic MRI cases. Overall, the proposed approach provides a robust and efficient tool for liver lesion characterization, with potential to support clinical decision-making and reduce diagnostic ambiguity in medical imaging practice.<\/jats:p>","DOI":"10.3390\/jimaging12090415","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T16:21:21Z","timestamp":1788452481000},"page":"415","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hybrid Fusion of Time\u2013Intensity Curve, Deep Learning, and Fractional Zernike\u2013Caputo Features for Accurate Liver Lesion Classification in DCE-MRI"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9151-6958","authenticated-orcid":false,"given":"Ali M.","family":"Hasan","sequence":"first","affiliation":[{"name":"College of Medicine, Al-Nahrain University, Baghdad 10001, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"software","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"given":"Noor K. N.","family":"Al-Waely","sequence":"additional","affiliation":[{"name":"College of Medicine, Al-Nahrain University, Baghdad 10001, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wallaa L.","family":"Alfalluji","sequence":"additional","affiliation":[{"name":"College of Medicine, Al-Mustaqbal University, Babil 51001, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9341-025X","authenticated-orcid":false,"given":"Rabha W.","family":"Ibrahim","sequence":"additional","affiliation":[{"name":"Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University, Nile Street, Thi-Qar 64001, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4823-6851","authenticated-orcid":false,"given":"Hamid A.","family":"Jalab","sequence":"additional","affiliation":[{"name":"Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University, Nile Street, Thi-Qar 64001, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"}]},{"given":"Farid","family":"Meziane","sequence":"additional","affiliation":[{"name":"Data Science Research Centre, School of Computing and Engineering, University of Derby, Derby DE22 3AW, UK"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1564","DOI":"10.1007\/s00261-024-04597-x","article-title":"Artificial intelligence for detection and characterization of focal hepatic lesions: A review","volume":"50","author":"Munuera","year":"2024","journal-title":"Abdom. Radiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"100624","DOI":"10.1016\/j.ejro.2024.100624","article-title":"Preliminary study on detection and diagnosis of focal liver lesions based on a deep learning model using multimodal PET\/CT images","volume":"14","author":"Luo","year":"2025","journal-title":"Eur. J. Radiol. Open"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.cmpb.2018.05.031","article-title":"A new deformable model based on fractional Wright energy function for tumor segmentation of volumetric brain MRI scans","volume":"163","author":"Ibrahim","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_4","first-page":"209","article-title":"Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA A Cancer J. Clin."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/S0720-048X(02)00053-0","article-title":"Ultrasonographic detection of focal liver lesions: Increased sensitivity and specificity with microbubble contrast agents","volume":"46","author":"Hohmann","year":"2003","journal-title":"Eur. J. Radiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"105002","DOI":"10.1016\/j.bspc.2023.105002","article-title":"A classification model of breast masses in DCE-MRI using kinetic curves features with quantum-Raina\u2019s polynomial based fusion","volume":"84","author":"Hasan","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"S42","DOI":"10.1102\/1470-7330.2004.0011","article-title":"Liver cancer imaging: Role of CT, MRI, US and PET","volume":"4","author":"Oliva","year":"2004","journal-title":"Cancer Imaging"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1016\/j.patcog.2015.10.011","article-title":"Fractional poisson enhancement model for text detection and recognition in video frames","volume":"52","author":"Roy","year":"2016","journal-title":"Pattern Recognit."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Omer, H.K., Jalab, H.A., Hasan, A.M., and Tawfiq, N.E. (December, January 29). Combination of local binary pattern and face geometric features for gender classification from face images. Proceedings of the 2019 9th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), Penang, Malaysia.","DOI":"10.1109\/ICCSCE47578.2019.9068593"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"107","DOI":"10.2174\/157340512800672216","article-title":"MRI of Focal Liver Lesions","volume":"8","author":"Albiin","year":"2012","journal-title":"Curr. Med. Imaging Rev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1053\/j.ro.2016.05.016","article-title":"Magnetic Resonanance Imaging of the Liver (Including Biliary Contrast Agents)-Part 2: Protocols for Liver Magnetic Resonanance Imaging and Characterization of Common Focal Liver Lesions","volume":"51","author":"Agostini","year":"2016","journal-title":"Semin. Roentgenol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"121371","DOI":"10.1016\/j.eswa.2023.121371","article-title":"Molecular subtypes classification of breast cancer in DCE-MRI using deep features","volume":"236","author":"Hasan","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Jalab, H.A., Subramaniam, T., Ibrahim, R.W., Kahtan, H., and Noor, N.F.M. (2019). New texture descriptor based on modified fractional entropy for digital image splicing forgery detection. Entropy, 21.","DOI":"10.3390\/e21040371"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yang, S.-N., Li, F.-J., Chen, J.-M., Zhang, G., Liao, Y.-H., and Huang, T.-C. (2016). Kinetic curve type assessment for classification of breast lesions using dynamic contrast-enhanced MR imaging. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0152827"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Schwarz, S., Clevert, D.A., Ingrisch, M., Geyer, T., Schwarze, V., R\u00fcbenthaler, J., and Armbruster, M. (2021). Quantitative Analysis of the Time-Intensity Curve of Contrast-Enhanced Ultrasound of the Liver: Differentiation of Benign and Malignant Liver Lesions. Diagnostics, 11.","DOI":"10.3390\/diagnostics11071244"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/S1665-2681(19)32034-4","article-title":"Hepatocellular carcinoma. An overview","volume":"5","author":"Uribe","year":"2006","journal-title":"Ann. Hepatol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"373","DOI":"10.4103\/mjdrdypu.mjdrdypu_144_22","article-title":"Time Intensity Curve in Primary Solid Hepatic Lesions: Does it Provide Objectivity to Otherwise Subjective Interpretation?","volume":"16","author":"Maurya","year":"2023","journal-title":"Med. J. Dr. D.Y. Patil Vidyapeeth"},{"key":"ref_18","first-page":"267","article-title":"Liver biopsy: Review of methodology and complications","volume":"13","author":"Tobkes","year":"1995","journal-title":"Dig. Dis."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1097\/MEG.0000000000000731","article-title":"Multiple biopsy passes and the risk of complications of percutaneous liver biopsy","volume":"29","author":"Chi","year":"2017","journal-title":"Eur. J. Gastroenterol. Hepatol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3213","DOI":"10.21037\/qims-21-1004","article-title":"Deep learning radiomics for focal liver lesions diagnosis on long-range contrast-enhanced ultrasound and clinical factors","volume":"12","author":"Liu","year":"2022","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1007\/s11220-025-00568-8","article-title":"Computer-Aided Diagnosis System for Classifying the Liver Lesions from Multiphase CT Images","volume":"26","author":"Nayantara","year":"2025","journal-title":"Sens. Imaging"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zossou, V.-B.S., Gnangnon, F.H.R., Biaou, O., de Vathaire, F., Allodji, R.S., and Ezin, E.C. (2024). Radiomics-Based Classification of Tumor and Healthy Liver on Computed Tomography Images. Cancers, 16.","DOI":"10.3390\/cancers16061158"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhou, J., Wang, W., Lei, B., Ge, W., Huang, Y., Zhang, L., Yan, Y., Zhou, D., Ding, Y., and Wu, J. (2021). Automatic detection and classification of focal liver lesions based on deep convolutional neural networks: A preliminary study. Front. Oncol., 10.","DOI":"10.3389\/fonc.2020.581210"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Du, L., Yuan, J., Gan, M., Li, Z., Wang, P., Hou, Z., and Wang, C. (2022). A comparative study between deep learning and radiomics models in grading liver tumors using hepatobiliary phase contrast-enhanced MR images. BMC Med. Imaging, 22.","DOI":"10.1186\/s12880-022-00946-8"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1186\/s13244-025-01904-y","article-title":"MRI-based deep learning radiomics to differentiate dual-phenotype hepatocellular carcinoma from HCC and intrahepatic cholangiocarcinoma: A multicenter study","volume":"16","author":"Wu","year":"2025","journal-title":"Insights Into Imaging"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Al-abayechi, A.A.A., Jalab, H.A., Ibrahim, R.W., and Hasan, A.M. (2017, January 28\u201330). Image enhancement based on fractional Poisson for segmentation of skin lesions using the watershed transform. Proceedings of the International Visual Informatics Conference, Bangi, Malaysia.","DOI":"10.1007\/978-3-319-70010-6_23"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"54","DOI":"10.14704\/nq.2020.18.9.NQ20216","article-title":"MRI Brain Scans Classification Using Bi-directional Modified Gray Level Co-occurrence Matrix and Long Short-Term Memory","volume":"18","author":"Hasan","year":"2020","journal-title":"NeuroQuantology"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hasan, A.M., Meziane, F., and Jalab, H.A. (2016, January 29\u201331). Performance of grey level statistic features versus Gabor wavelet for screening MRI brain tumors: A comparative study. Proceedings of the 2016 6th International Conference on Information Communication and Management (ICICM), Hatfield, UK.","DOI":"10.1109\/INFOCOMAN.2016.7784230"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"107546","DOI":"10.1016\/j.bspc.2025.107546","article-title":"Discrimination of invasive ductal and lobular carcinoma of the breast based on the combination of enhanced Legendre polynomial, kinetic features and deep learning features","volume":"104","author":"Hasan","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_30","first-page":"2623","article-title":"Classification of breast mass lesions on dynamic contrast-enhanced magnetic resonance imaging by a computer-assisted diagnosis system based on quantitative analysis","volume":"17","author":"Yin","year":"2019","journal-title":"Oncol. Lett."},{"key":"ref_31","first-page":"107","article-title":"Automatic Prostate Cancer Segmentation Using Kinetic Analysis in Dynamic Contrast-Enhanced MRI","volume":"8","author":"Mostaar","year":"2018","journal-title":"J. Biomed. Phys. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"20210220","DOI":"10.1259\/bjr.20210220","article-title":"Quantitative magnetic resonance imaging for focal liver lesions: Bridging the gap between research and clinical practice","volume":"94","author":"Cannella","year":"2021","journal-title":"Br. J. Radiol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"41","DOI":"10.14704\/nq.2021.19.7.NQ21082","article-title":"Magnetic resonance imaging breast scan classification based on texture features and long short-term memory model","volume":"19","author":"Hilal","year":"2021","journal-title":"NeuroQuantology"},{"key":"ref_34","unstructured":"Jalab, H.A., Ibrahim, R.W., Jalab, D., Jalab, A., and Hasan, A. (2017, January 18\u201320). Medical image enhancement based on statistical distributions in fractional calculus. Proceedings of the Computing Conference, London, UK."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4612","DOI":"10.21037\/qims-22-74","article-title":"The feasibility of superparamagnetic iron oxide\u2013enhanced magnetic resonance imaging for assessing liver lesions in patients with contraindications for iodine CT contrast media or gadolinium-based MR contrast media: A retrospective case-control study","volume":"12","author":"Kurata","year":"2022","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"20170959","DOI":"10.1259\/bjr.20170959","article-title":"Liver fat imaging-a clinical overview of ultrasound, CT, and MR imaging","volume":"91","author":"Zhang","year":"2018","journal-title":"Br. J. Radiol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"100535","DOI":"10.1016\/j.ejro.2023.100535","article-title":"Assessment of LI-RADS efficacy in classification of hepatocellular carcinoma and benign liver nodules using DCE-MRI features and machine learning","volume":"11","author":"Fotouhi","year":"2023","journal-title":"Eur. J. Radiol. Open"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1186\/s13244-025-01968-w","article-title":"A deep learning model based on self-supervised learning for identifying subtypes of proliferative hepatocellular carcinoma from dynamic contrast-enhanced MRI","volume":"16","author":"Qu","year":"2025","journal-title":"Insights Into Imaging"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/9\/415\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T16:40:41Z","timestamp":1788453641000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/9\/415"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,3]]},"references-count":38,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["jimaging12090415"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12090415","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,3]]}}}