{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T17:38:51Z","timestamp":1726249131482},"reference-count":50,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,20]],"date-time":"2022-07-20T00:00:00Z","timestamp":1658275200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002383","name":"Deanship of Scientific Research at King Saud University","doi-asserted-by":"publisher","award":["RG-1438-071"],"id":[{"id":"10.13039\/501100002383","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Liver cancer is a life-threatening illness and one of the fastest-growing cancer types in the world. Consequently, the early detection of liver cancer leads to lower mortality rates. This work aims to build a model that will help clinicians determine the type of tumor when it occurs within the liver region by analyzing images of tissue taken from a biopsy of this tumor. Working within this stage requires effort, time, and accumulated experience that must be possessed by a tissue expert to determine whether this tumor is malignant and needs treatment. Thus, a histology expert can make use of this model to obtain an initial diagnosis. This study aims to propose a deep learning model using convolutional neural networks (CNNs), which are able to transfer knowledge from pre-trained global models and decant this knowledge into a single model to help diagnose liver tumors from CT scans. Thus, we obtained a hybrid model capable of detecting CT images of a biopsy of a liver tumor. The best results that we obtained within this research reached an accuracy of 0.995, a precision value of 0.864, and a recall value of 0.979, which are higher than those obtained using other models. It is worth noting that this model was tested on a limited set of data and gave good detection results. This model can be used as an aid to support the decisions of specialists in this field and save their efforts. In addition, it saves the effort and time incurred by the treatment of this type of cancer by specialists, especially during periodic examination campaigns every year.<\/jats:p>","DOI":"10.3390\/s22145429","type":"journal-article","created":{"date-parts":[[2022,7,21]],"date-time":"2022-07-21T07:34:40Z","timestamp":1658388880000},"page":"5429","source":"Crossref","is-referenced-by-count":14,"title":["Automatic Detection of Liver Cancer Using Hybrid Pre-Trained Models"],"prefix":"10.3390","volume":"22","author":[{"given":"Esam","family":"Othman","sequence":"first","affiliation":[{"name":"Faculty of Applied Computer Science, King Saud University, Riyadh 11451, Saudi Arabia"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-0618-5662","authenticated-orcid":false,"given":"Muhammad","family":"Mahmoud","sequence":"additional","affiliation":[{"name":"Department of Information Systems, Madina Higher Institute of Management and Technology, Shabramant 12947, Egypt"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-4668-7840","authenticated-orcid":false,"given":"Habib","family":"Dhahri","sequence":"additional","affiliation":[{"name":"Faculty of Applied Computer Science, King Saud University, Riyadh 11451, Saudi Arabia"}]},{"given":"Hatem","family":"Abdulkader","sequence":"additional","affiliation":[{"name":"Department of Information Systems, Faculty of Computers and Information, Menoufia University, Shebin El-kom 32511, Menoufia, Egypt"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-4163-7625","authenticated-orcid":false,"given":"Awais","family":"Mahmood","sequence":"additional","affiliation":[{"name":"Faculty of Applied Computer Science, King Saud University, Riyadh 11451, Saudi Arabia"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-8592-6851","authenticated-orcid":false,"given":"Mina","family":"Ibrahim","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Faculty of Computers and Information, Menoufia University, Shebin El-kom 32511, Menoufia, Egypt"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,20]]},"reference":[{"key":"ref_1","first-page":"7","article-title":"Cancer statistics, 2022","volume":"72","author":"Siegel","year":"2022","journal-title":"CA"},{"key":"ref_2","first-page":"9919507","article-title":"CT image segmentation method of liver tumor based on artificial intelligence enabled medical imaging","volume":"2021","author":"Liu","year":"2021","journal-title":"Math. Probl. Eng."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1016\/S1470-2045(07)70170-5","article-title":"Abdominal MRI advances in the detection of liver tumours and characterisation","volume":"8","author":"Low","year":"2007","journal-title":"Lancet Oncol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.dld.2006.03.021","article-title":"The role of bright liver echo pattern on ultrasound B-mode examination in the diagnosis of liver steatosis","volume":"38","author":"Palmentieri","year":"2006","journal-title":"Dig. Liver Dis."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1007\/s11023-007-9079-x","article-title":"Universal intelligence: A definition of machine intelligence","volume":"17","author":"Legg","year":"2007","journal-title":"Minds Mach."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"94780","DOI":"10.1109\/ACCESS.2021.3088341","article-title":"Deep learning modalities for biometric alteration detection in 5G networks-based secure smart cities","volume":"9","author":"Sedik","year":"2021","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"26527","DOI":"10.1109\/ACCESS.2018.2886573","article-title":"Multimodal biometric authentication systems using convolution neural network based on different level fusion of ECG and fingerprint","volume":"7","author":"Hammad","year":"2018","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"83581","DOI":"10.1109\/ACCESS.2019.2923856","article-title":"Biometric recognition using multimodal physiological signals","volume":"7","author":"Bianco","year":"2019","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"135184","DOI":"10.1109\/ACCESS.2020.3010326","article-title":"An accurate and fast cardio-views classification system based on fused deep features and LSTM","volume":"8","author":"Shahin","year":"2020","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"8054","DOI":"10.1109\/ACCESS.2021.3049522","article-title":"Fitness monitoring system based on internet of things and big data analysis","volume":"9","author":"Qiu","year":"2021","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1512","DOI":"10.1016\/j.bbe.2020.08.007","article-title":"Development of novel ensemble model using stacking learning and evolutionary computation techniques for automated hepatocellular carcinoma detection","volume":"40","author":"Hammad","year":"2020","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"680","DOI":"10.3389\/fonc.2020.00680","article-title":"Deep learning for accurate diagnosis of liver tumor based on magnetic resonance imaging and clinical data","volume":"10","author":"Zhen","year":"2020","journal-title":"Front. Oncol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3338","DOI":"10.1007\/s00330-019-06205-9","article-title":"Deep learning for liver tumor diagnosis part I: Development of a convolutional neural network classifier for multi-phasic MRI","volume":"29","author":"Hamm","year":"2019","journal-title":"Eur. Radiol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Polaka, I., Bhandari, M.P., Mezmale, L., Anarkulova, L., Veliks, V., Sivins, A., Lescinska, A.M., Tolmanis, I., Vilkoite, I., and Ivanovs, I. (2022). Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection. Diagnostics, 12.","DOI":"10.3390\/diagnostics12020491"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1016\/j.ins.2021.05.035","article-title":"Automated detection of shockable ECG signals: A review","volume":"571","author":"Hammad","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.cogsys.2018.12.001","article-title":"A novel machine learning approach for early detection of hepatocellular carcinoma patients","volume":"54","author":"Abdar","year":"2019","journal-title":"Cogn. Syst. Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"103567","DOI":"10.1016\/j.bspc.2022.103567","article-title":"2.5 D lightweight RIU-Net for automatic liver and tumor segmentation from CT","volume":"75","author":"Lv","year":"2022","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"103661","DOI":"10.1016\/j.bspc.2022.103661","article-title":"A novel model to optimize multiple imputation algorithm for missing data using evolution methods","volume":"76","author":"Mohammed","year":"2022","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"78214","DOI":"10.1109\/ACCESS.2018.2884896","article-title":"Hybrid feature selection method based on neural networks and cross-validation for liver cancer with microarray","volume":"6","author":"Kim","year":"2018","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"85271","DOI":"10.1109\/ACCESS.2020.2992627","article-title":"SCCNN: A diagnosis method for hepatocellular carcinoma and intrahepatic cholangiocarcinoma based on siamese cross contrast neural network","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.cogsys.2018.12.009","article-title":"Deep learning based liver cancer detection using watershed transform and Gaussian mixture model techniques","volume":"54","author":"Das","year":"2019","journal-title":"Cogn. Syst. Res."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ghoniem, R.M. (2020). A novel bio-inspired deep learning approach for liver cancer diagnosis. Information, 11.","DOI":"10.3390\/info11020080"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"129889","DOI":"10.1109\/ACCESS.2020.3006362","article-title":"Liver cancer detection using hybridized fully convolutional neural network based on deep learning framework","volume":"8","author":"Dong","year":"2020","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5763","DOI":"10.1007\/s12652-020-02107-7","article-title":"Improved performance accuracy in detecting tumor in liver using deep learning techniques","volume":"12","author":"Sureshkumar","year":"2021","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"115686","DOI":"10.1016\/j.eswa.2021.115686","article-title":"An automated slice sorting technique for multi-slice computed tomography liver cancer images using convolutional network","volume":"186","author":"Kaur","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4277436","DOI":"10.1155\/2022\/4277436","article-title":"AI-DRIVEN novel approach for liver cancer screening and prediction using cascaded fully convolutional neural network","volume":"2022","author":"Shukla","year":"2022","journal-title":"J. Healthc. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1549","DOI":"10.1007\/s11548-021-02410-4","article-title":"LiverNet: Efficient and robust deep learning model for automatic diagnosis of sub-types of liver hepatocellular carcinoma cancer from H&E stained liver histopathology images","volume":"16","author":"Aatresh","year":"2021","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1643","DOI":"10.1109\/JBHI.2019.2949837","article-title":"Deep learning-based classification of liver cancer histopathology images using only global labels","volume":"24","author":"Sun","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4729","DOI":"10.1007\/s11042-020-09900-8","article-title":"Deep learning for liver tumour classification: Enhanced loss function","volume":"80","author":"Randhawa","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"76056","DOI":"10.1109\/ACCESS.2020.2988647","article-title":"Deep learning initialized and gradient enhanced level-set based segmentation for liver tumor from CT images","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_32","first-page":"1","article-title":"Liver tumor segmentation and classification: A systematic review","volume":"2020","author":"Rela","year":"2020","journal-title":"IEEE HYDCON"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"868","DOI":"10.1007\/s12072-021-10229-z","article-title":"The overview of the deep learning integrated into the medical imaging of liver: A review","volume":"15","author":"Xiang","year":"2021","journal-title":"Hepatol. Int."},{"key":"ref_34","unstructured":"Soler, L., Hostettler, A., Agnus, V., Charnoz, A., Fasquel, J., Moreau, J., Osswald, A., Bouhadjar, M., and Marescaux, J. (2010). 3D Image Reconstruction for Comparison of Algorithm Database: A Patient Specific Anatomical and Medical Image Database, IRCAD."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5546","DOI":"10.1016\/j.csbj.2021.10.006","article-title":"Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis","volume":"19","author":"Kourou","year":"2021","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_36","unstructured":"Bilic, P., Christ, P.F., Vorontsov, E., Chlebus, G., Chen, H., Dou, Q., Fu, C., Han, X., Heng, P., and Hesser, J. (2019). The liver tumor segmentation benchmark (LiTs). arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Yurtkulu, S.C., \u015eahin, Y.H., and Unal, G. (2019, January 24\u201326). Semantic segmentation with extended DeepLabv3 architecture. Proceedings of the 2019 27th Signal Processing and Communications Applications Conference (SIU), Sivas, Turkey.","DOI":"10.1109\/SIU.2019.8806244"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Al-Haija, Q.A., and Adebanjo, A. (2020, January 9\u201312). Breast cancer diagnosis in histopathological images using ResNet-50 convolutional neural network. Proceedings of the 2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), Vancouver, BC, Canada.","DOI":"10.1109\/IEMTRONICS51293.2020.9216455"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"104036","DOI":"10.1016\/j.compbiomed.2020.104036","article-title":"A comparative study of pre-trained convolutional neural networks for semantic segmentation of breast tumors in ultrasound","volume":"126","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_40","unstructured":"Mubarak, D.M.N. (IRBM, 2021). Classification of early stages of esophageal cancer using transfer learning, IRBM, in press."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"e607","DOI":"10.7717\/peerj-cs.607","article-title":"Chest X-ray pneumothorax segmentation using U-Net with EfficientNet and ResNet architectures","volume":"7","author":"Abedalla","year":"2021","journal-title":"PeerJ Comput. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"108881","DOI":"10.1016\/j.knosys.2022.108881","article-title":"Fused information of DeepLabv3+ and transfer learning model for semantic segmentation and rich features selection using equilibrium optimizer (EO) for classification of NPDR lesions","volume":"249","author":"Amin","year":"2022","journal-title":"Knowl. Based Syst."},{"key":"ref_43","first-page":"143","article-title":"Transfer learning using VGG-16 with deep convolutional neural network for classifying images","volume":"9","author":"Tammina","year":"2019","journal-title":"Int. J. Sci. Res. Publ."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Sertic, P., Alahmar, A., Akilan, T., Javorac, M., and Gupta, Y. (2022). Intelligent Real-Time Face-Mask Detection System with Hardware Acceleration for COVID-19 Mitigation. Healthcare, 10.","DOI":"10.3390\/healthcare10050873"},{"key":"ref_45","unstructured":"Iglovikov, V., and Shvets, A. (2018). Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/s10916-019-1243-3","article-title":"LSTM model for prediction of heart failure in big data","volume":"43","author":"Maragatham","year":"2019","journal-title":"J. Med. Syst."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Awassa, L., Jdey, I., Dhahri, H., Hcini, G., Mahmood, A., Othman, E., and Haneef, M. (2022). Study of Different Deep Learning Methods for Coronavirus (COVID-19) Pandemic: Taxonomy, Survey and Insights. Sensors, 22.","DOI":"10.3390\/s22051890"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"11881","DOI":"10.1007\/s11042-021-11889-7","article-title":"A multiple organ segmentation system for CT image series using Attention-LSTM fused U-Net","volume":"81","author":"Chen","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"115008","DOI":"10.1016\/j.eswa.2021.115008","article-title":"Study on strategy of CT image sequence segmentation for liver and tumor based on U-Net and Bi-ConvLSTM","volume":"180","author":"Li","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_50","unstructured":"Liu, M., Chen, L., Du, X., Jin, L., and Shang, M. (2021). Activated gradients for deep neural networks. IEEE Trans. Neural Netw. Learn. Syst., 1\u201313."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/14\/5429\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T12:22:15Z","timestamp":1722601335000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/14\/5429"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,20]]},"references-count":50,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["s22145429"],"URL":"https:\/\/doi.org\/10.3390\/s22145429","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,7,20]]}}}
