{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T19:24:53Z","timestamp":1789500293143,"version":"build-2803163510"},"reference-count":58,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T00:00:00Z","timestamp":1673913600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Wan Azani Mustafa"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Atrophic gastritis (AG) is commonly caused by the infection of the Helicobacter pylori (H. pylori) bacteria. If untreated, AG may develop into a chronic condition leading to gastric cancer, which is deemed to be the third primary cause of cancer-related deaths worldwide. Precursory detection of AG is crucial to avoid such cases. This work focuses on H. pylori-associated infection located at the gastric antrum, where the classification is of binary classes of normal versus atrophic gastritis. Existing work developed the Deep Convolution Neural Network (DCNN) of GoogLeNet with 22 layers of the pre-trained model. Another study employed GoogLeNet based on the Inception Module, fast and robust fuzzy C-means (FRFCM), and simple linear iterative clustering (SLIC) superpixel algorithms to identify gastric disease. GoogLeNet with Caffe framework and ResNet-50 are machine learners that detect H. pylori infection. Nonetheless, the accuracy may become abundant as the network depth increases. An upgrade to the current standards method is highly anticipated to avoid untreated and inaccurate diagnoses that may lead to chronic AG. The proposed work incorporates improved techniques revolving within DCNN with pooling as pre-trained models and channel shuffle to assist streams of information across feature channels to ease the training of networks for deeper CNN. In addition, Canonical Correlation Analysis (CCA) feature fusion method and ReliefF feature selection approaches are intended to revamp the combined techniques. CCA models the relationship between the two data sets of significant features generated by pre-trained ShuffleNet. ReliefF reduces and selects essential features from CCA and is classified using the Generalized Additive Model (GAM). It is believed the extended work is justified with a 98.2% testing accuracy reading, thus providing an accurate diagnosis of normal versus atrophic gastritis.<\/jats:p>","DOI":"10.3390\/diagnostics13030336","type":"journal-article","created":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T06:25:46Z","timestamp":1673936746000},"page":"336","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["H. pylori Related Atrophic Gastritis Detection Using Enhanced Convolution Neural Network (CNN) Learner"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2757-6347","authenticated-orcid":false,"given":"Yasmin Mohd","family":"Yacob","sequence":"first","affiliation":[{"name":"Faculty of Electronic Engineering & Technology, Pauh Putra Campus, Universiti Malaysia Perlis (UniMAP), Arau 02600, Perlis, Malaysia"},{"name":"Centre of Excellence for Advanced Computing, Pauh Putra Campus, Universiti Malaysia Perlis (UniMAP), Arau 02600, Perlis, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2966-6442","authenticated-orcid":false,"given":"Hiam","family":"Alquran","sequence":"additional","affiliation":[{"name":"Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid 21163, Jordan"},{"name":"Department of Biomedical Engineering, Jordan University of Science and Technology, Irbid 22110, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7260-6085","authenticated-orcid":false,"given":"Wan Azani","family":"Mustafa","sequence":"additional","affiliation":[{"name":"Centre of Excellence for Advanced Computing, Pauh Putra Campus, Universiti Malaysia Perlis (UniMAP), Arau 02600, Perlis, Malaysia"},{"name":"Faculty of Electrical Engineering & Technology, Pauh Putra Campus, Universiti Malaysia Perlis (UniMAP), Arau 02600, Perlis, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5450-3311","authenticated-orcid":false,"given":"Mohammed","family":"Alsalatie","sequence":"additional","affiliation":[{"name":"King Hussein Medical Center, Royal Jordanian Medical Service, The Institute of Biomedical Technology, Amman 11855, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8284-2054","authenticated-orcid":false,"given":"Harsa Amylia Mat","family":"Sakim","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal 11800, Penang, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9287-7317","authenticated-orcid":false,"given":"Muhamad Safiih","family":"Lola","sequence":"additional","affiliation":[{"name":"Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Terengganu 21030, Terengganu, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Goyal, H., Mann, R., Gandhi, Z., Perisetti, A., Ali, A., Aman Ali, K., Sharma, N., Saligram, S., Tharian, B., and Inamdar, S. (2020). Scope of Artificial Intelligence in Screening and Diagnosis of Colorectal Cancer. J. Clin. Med., 9.","DOI":"10.3390\/jcm9103313"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1148\/radiol.211785","article-title":"Artificial Intelligence in Fracture Detection: A Systematic Review and Meta-Analysis","volume":"304","author":"Kuo","year":"2022","journal-title":"Radiology"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"041503","DOI":"10.1063\/5.0011697","article-title":"Artificial intelligence for brain diseases: A systematic review","volume":"4","author":"Segato","year":"2020","journal-title":"APL Bioeng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"6963","DOI":"10.21037\/jtd-21-761","article-title":"Artificial intelligence in thoracic surgery: A narrative review","volume":"13","author":"Bellini","year":"2021","journal-title":"J. Thorac."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"38","DOI":"10.4103\/jpi.jpi_53_18","article-title":"Artificial Intelligence and Digital Pathology: Challenges and Opportunities","volume":"9","author":"Tizhoosh","year":"2018","journal-title":"J. Pathol. Inform."},{"key":"ref_6","unstructured":"Savage, N. (2020). Another Set of Eyes for Cancer Diagnostics, Springer Nature."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e25442","DOI":"10.2196\/25442","article-title":"An Artificial Intelligence Model to Predict the Mortality of COVID-19 Patients at Hospital Admission Time Using Routine Blood Samples: Development and Validation of an Ensemble Model","volume":"22","author":"Ko","year":"2020","journal-title":"J. Med. Internet Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"94","DOI":"10.7861\/futurehosp.6-2-94","article-title":"The potential for artificial intelligence in healthcare","volume":"6","author":"Davenport","year":"2019","journal-title":"Future Healthc. J."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Weis, C.-A., Weihrauch, K.R., Kriegsmann, K., and Kriegsmann, M. (2022). Unsupervised Segmentation in NSCLC: How to Map the Output of Unsupervised Segmentation to Meaningful Histological Labels by Linear Combination?. Appl. Sci., 12.","DOI":"10.3390\/app12083718"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Park, S., Kim, H., Shim, E., Hwang, B.-Y., Kim, Y., Lee, J.-W., and Seo, H. (2022). Deep Learning-Based Automatic Segmentation of Mandible and Maxilla in Multi-Center CT Images. Appl. Sci., 12.","DOI":"10.3390\/app12031358"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wu, S., Wu, Y., Chang, H., Su, F.T., Liao, H., Tseng, W., Liao, C., Lai, F., Hsu, F., and Xiao, F. (2021). Deep Learning-Based Segmentation of Various Brain Lesions for Radiosurgery. Appl. Sci., 11.","DOI":"10.3390\/app11199180"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Asami, Y., Yoshimura, T., Manabe, K., Yamada, T., and Sugimori, H. (2021). Development of Detection and Volumetric Methods for the Triceps of the Lower Leg Using Magnetic Resonance Images with Deep Learning. Appl. Sci., 11.","DOI":"10.3390\/app112412006"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Karhade, J., Ghosh, S.K., Gajbhiye, P., Tripathy, R.K., and Rajendra Acharya, U. (2021). Multichannel Multiscale Two-Stage Convolutional Neural Network for the Detection and Localization of Myocardial Infarction Using Vectorcardiogram Signal. Appl. Sci., 11.","DOI":"10.3390\/app11177920"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Malik, S., Akram, T., Ashraf, I., Rafiullah, M., Ullah, M., and Tanveer, J. (2022). A Hybrid Preprocessor DE-ABC for Efficient Skin-Lesion Segmentation with Improved Contrast. Diagnostics, 12.","DOI":"10.3390\/diagnostics12112625"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lv, Z., Poiesi, F., Dong, Q., Lloret, J., and Song, H. (2022). Deep Learning for Intelligent Human\u2013Computer Interaction. Appl. Sci., 12.","DOI":"10.3390\/app122211457"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Mo, S., Lu, P., and Liu, X. (2022). AI-Generated Face Image Identification with Different Color Space Channel Combinations. Sensors, 22.","DOI":"10.3390\/s22218228"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Baazaoui, H., Hubertus, S., Maros, M.E., Mohamed, S.A., F\u00f6rster, A., Schad, L.R., and Wenz, H. (2021). Artificial Neural Network-Derived Cerebral Metabolic Rate of Oxygen for Differentiating Glioblastoma and Brain Metastasis in MRI: A Feasibility Study. Appl. Sci., 11.","DOI":"10.3390\/app11219928"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Taibouni, K., Miere, A., Samake, A., Souied, E., Petit, E., and Chenoune, Y. (2021). Choroidal Neovascularization Screening on OCTAngiography Choriocapillaris Images by Convolutional Neural Networks. Appl. Sci., 11.","DOI":"10.3390\/app11199313"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Cazzato, G., Massaro, A., Colagrande, A., Lettini, T., Cicco, S., Parente, P., Nacchiero, E., Lospalluti, L., Cascardi, E., and Giudice, G. (2022). Dermatopathology of Malignant Melanoma in the Era of Artificial Intelligence: A Single Institutional Experience. Diagnostics, 12.","DOI":"10.3390\/diagnostics12081972"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Massaro, A., Dipierro, G., Cannella, E., and Galiano, A.M. (2020). Comparative Analysis among Discrete Fourier Transform, K-Means and Artificial Neural Networks Image Processing Techniques Oriented on Quality Control of Assembled Tires. Information, 11.","DOI":"10.3390\/info11050257"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Mohimont, L., Alin, F., Rondeau, M., Gaveau, N., and Steffenel, L.A. (2022). Computer Vision and Deep Learning for Precision Viticulture. Agronomy, 12.","DOI":"10.3390\/agronomy12102463"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Massaro, A., Panarese, A., Selicato, S., and Galiano, A. (2022, January 7\u20139). CNN-LSTM Neural Network Applied for Thermal Infrared Underground Water Leakage. Proceedings of the IEEE International Workshop on Metrology for Industry 4.0 and IOT (MetroInd4.0&IoT), Trento, Italy.","DOI":"10.1109\/MetroInd4.0IoT51437.2021.9488536"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e21983","DOI":"10.2196\/21983","article-title":"Artificial Intelligence for the Prediction of Helicobacter Pylori Infection in Endoscopic Images: Systematic Review and Meta-Analysis of Diagnostic Test Accuracy","volume":"22","author":"Bang","year":"2020","journal-title":"J. Med. Internet Res."},{"key":"ref_24","first-page":"20","article-title":"Convolutional neural networks in the computer-aided diagnosis of Helicobacter pylori infection and non-causal comparison to physician endoscopists: A systematic review with meta-analysis","volume":"34","author":"Mohan","year":"2021","journal-title":"Ann. Gastroenterol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Pecere, S., Milluzzo, S.M., Esposito, G., Dilaghi, E., Telese, A., and Eusebi, L.H. (2021). Applications of Artificial Intelligence for the Diagnosis of Gastrointestinal Diseases. Diagnostics, 11.","DOI":"10.3390\/diagnostics11091575"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"E1584","DOI":"10.1055\/a-1236-3007","article-title":"Accuracy of convolutional neural network-based artificial intelligence in diagnosis of gastrointestinal lesions based on endoscopic images: A systematic review and meta-analysis","volume":"8","author":"Mohan","year":"2020","journal-title":"Endosc. Int. Open"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Cardos, A.I., Maghiar, A., Zaha, D.C., Pop, O., Fritea, L., Miere, F., and Cavalu, S. (2022). Evolution of Diagnostic Methods for Helicobacter pylori Infections: From Traditional Tests to High Technology, Advanced Sensitivity and Discrimination Tools. Diagnostics, 12.","DOI":"10.3390\/diagnostics12020508"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.ebiom.2017.10.014","article-title":"Application of convolutional neural networks in the diagnosis of Helicobacter pylori infection based on endoscopic images","volume":"25","author":"Shichijo","year":"2017","journal-title":"EBioMedicine"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"E139","DOI":"10.1055\/s-0043-120830","article-title":"Deep learning analyzes Helicobacter pylori infection by upper gastrointestinal endoscopy images","volume":"6","author":"Itoh","year":"2018","journal-title":"Endosc. Int. Open"},{"key":"ref_30","first-page":"462","article-title":"Artificial intelligence diagnosis of Helicobacter pylori infection using blue laser imaging-bright and linked color imaging: A single-center prospective study","volume":"31","author":"Nakashima","year":"2018","journal-title":"Ann. Gastroenterol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1080\/00365521.2019.1577486","article-title":"Application of convolutional neural networks for evaluating Helicobacter pylori infection status on the basis of endoscopic images","volume":"54","author":"Shichijo","year":"2019","journal-title":"Scand. J. Gastroenterol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e00109","DOI":"10.14309\/ctg.0000000000000109","article-title":"High Accuracy of Convolutional Neural Network for Evaluation of Helicobacter pylori Infection Based on Endoscopic Images: Preliminary Experience","volume":"10","author":"Zheng","year":"2019","journal-title":"Clin. Transl. Gastroenterol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2549","DOI":"10.1007\/s42835-019-00259-x","article-title":"Gastric Lesion Classification Using Deep Learning Based on Fast and Robust Fuzzy C-Means and Simple Linear Iterative Clustering Superpixel Algorithms","volume":"14","year":"2019","journal-title":"J. Electr. Eng. Technol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1136\/gutjnl-2019-319347","article-title":"Deep-learning based detection of gastric precancerous conditions","volume":"69","author":"Keller","year":"2020","journal-title":"Gut"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Kim, H.H., Lim, Y.S., Seo, S.-I., Lee, K.J., Kim, J.Y., and Shin, W.G. (2021). A Deep Recurrent Neural Network-Based Explainable Prediction Model for Progression from Atrophic Gastritis to Gastric Cancer. Appl. Sci., 11.","DOI":"10.3390\/app11136194"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1109\/TITB.2007.913128","article-title":"Helicobacter pylori-related gastric histology classification using support-vector-machine-based feature selection","volume":"12","author":"Huang","year":"2008","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1339","DOI":"10.1016\/j.gie.2017.11.029","article-title":"Computer-aided diagnosis for identifying and delineating early gastric cancers in magnifying narrow-band imaging","volume":"87","author":"Kanesaka","year":"2018","journal-title":"Gastrointest Endosc."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1111\/den.13509","article-title":"Potential of automatic diagnosis system with linked color imaging for diagnosis of Helicobacter pylori infection","volume":"32","author":"Yasuda","year":"2020","journal-title":"Dig. Endosc."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Chapelle, N., Osmola, M., Martin, J., Blin, J., Leroy, M., Jirka, I., Moussata, D., Lamarque, D., Olivier, R., and Tougeron, D. (2022). Serum Pepsinogens Combined with New Biomarkers Testing Using Chemiluminescent Enzyme Immunoassay for Non-Invasive Diagnosis of Atrophic Gastritis: A Prospective, Multicenter Study. Diagnostics, 12.","DOI":"10.3390\/diagnostics12030695"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"7497","DOI":"10.1038\/s41598-018-25842-6","article-title":"Automatic anatomical classification of esophagogastroduodenoscopy images using deep convolutional neural networks","volume":"14","author":"Takiyama","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1055\/a-0855-3532","article-title":"A deep neural network improves endoscopic detection of early gastric cancer without blind spots","volume":"51","author":"Wu","year":"2019","journal-title":"Endoscopy"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1007\/s10120-018-0793-2","article-title":"Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic image","volume":"21","author":"Hirasawa","year":"2018","journal-title":"Gastric Cancer"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"e34","DOI":"10.1111\/den.13306","article-title":"Detecting gastric cancer from video images using convolutional neural networks","volume":"31","author":"Ishioka","year":"2019","journal-title":"Dig. Endosc."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1007\/s10120-019-00992-2","article-title":"Convolutional neural network for the diagnosis of early gastric cancer based on magnifying narrow band imaging","volume":"23","author":"Li","year":"2020","journal-title":"Gastric Cancer"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1355","DOI":"10.1007\/s10620-019-05862-6","article-title":"Convolutional neural network for differentiating gastric cancer from gastritis using magnified endoscopy with narrow band imaging","volume":"65","author":"Horiuchi","year":"2020","journal-title":"Dig. Dis. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1016\/j.gie.2018.11.011","article-title":"Application of convolutional neural network in the diagnosis of the invasion depth of gastric cancer based on conventional endoscopy","volume":"89","author":"Zhu","year":"2019","journal-title":"Gastrointest. Endosc."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Alquran, H., Alsalatie, M., Mustafa, W.A., Abdi, R.A., and Ismail, A.R. (2022). Cervical Net: A Novel Cervical Cancer Classification Using Feature Fusion. Bioengineering, 9.","DOI":"10.3390\/bioengineering9100578"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Alquran, H., Al-Issa, Y., Alsalatie, M., Mustafa, W.A., Qasmieh, I.A., and Zyout, A. (2022). Intelligent Diagnosis and Classification of Keratitis. Diagnostics, 12.","DOI":"10.3390\/diagnostics12061344"},{"key":"ref_49","first-page":"5117","article-title":"Cervical Cancer Classification Using Combined Machine Learning and Deep Learning Approach","volume":"72","author":"Alquran","year":"2022","journal-title":"Comput. Mater. Contin."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"109603","DOI":"10.1016\/j.ymssp.2022.109603","article-title":"Canonical correlation analysis of dimension reduced degradation feature space for machinery condition monitoring","volume":"182","author":"Li","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1093\/biomet\/28.3-4.321","article-title":"Relations between two sets of variates","volume":"28","author":"Hotelling","year":"1936","journal-title":"Biometrika"},{"key":"ref_52","unstructured":"Anderson, T.W. (2003). An Introduction to Multivariate Statistical Analysis, John Willey and Sons. [3rd ed.]."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"101644","DOI":"10.1016\/j.bspc.2019.101644","article-title":"Enhanced use practices in SSVEP-based BCIs using an analytical approach of canonical correlation analysis","volume":"55","author":"Brogin","year":"2020","journal-title":"Biomed. Signal Process."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Ali, M.H., and Baiee, W.R. (2021, January 23\u201324). Choosing an Appropriate Feature Selection Method to Enhance Feed-Forward ANN. Proceedings of the International Conference on Communication and Information Technology, ICICT, Basrah, Iraq.","DOI":"10.1109\/ICICT52195.2021.9568428"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Bergadano, F., and De Raedt, L. (1994). Estimating attributes: Analysis and extensions of RELIEF. Machine Learning: ECML-94. ECML 1994, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/3-540-57868-4"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zhang, Y. (2022). A Hybrid Convolutional Neural Network and Relief-F Algorithm for Fault Power Line Recognition in Internet of Things-Based Smart Grids. Wirel. Commun. Mob. Comput., 4911553.","DOI":"10.1155\/2022\/4911553"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"109368","DOI":"10.1016\/j.ecolind.2022.109368","article-title":"A hybrid CART-GAMs model to evaluate benthic macroinvertebrate habitat suitability in the Pearl River Estuary, China","volume":"143","author":"Shen","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_58","first-page":"611","article-title":"Severe upper gastrointestinal bleeding determined by a gastric lymphoma associated with Helicobacter pylori-positive atrophic gastritis","volume":"58","author":"Vere","year":"2017","journal-title":"Rom. J. Morphol. Embryol."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/3\/336\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:08:20Z","timestamp":1760119700000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/3\/336"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,17]]},"references-count":58,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["diagnostics13030336"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics13030336","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,17]]}}}