{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T12:09:08Z","timestamp":1783166948333,"version":"3.54.6"},"reference-count":62,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T00:00:00Z","timestamp":1711497600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The advent of deep learning (DL) has revolutionized medical imaging, offering unprecedented avenues for accurate disease classification and diagnosis. DL models have shown remarkable promise for classifying brain tumors from Magnetic Resonance Imaging (MRI) scans. However, despite their impressive performance, the opaque nature of DL models poses challenges in understanding their decision-making mechanisms, particularly crucial in medical contexts where interpretability is essential. This paper explores the intersection of medical image analysis and DL interpretability, aiming to elucidate the decision-making rationale of DL models in brain tumor classification. Leveraging ten state-of-the-art DL frameworks with transfer learning, we conducted a comprehensive evaluation encompassing both classification accuracy and interpretability. These models underwent thorough training, testing, and fine-tuning, resulting in EfficientNetB0, DenseNet121, and Xception outperforming the other models. These top-performing models were examined using adaptive path-based techniques to understand the underlying decision-making mechanisms. Grad-CAM and Grad-CAM++ highlighted critical image regions where the models identified patterns and features associated with each class of the brain tumor. The regions where the models identified patterns and features correspond visually to the regions where the tumors are located in the images. This result shows that DL models learn important features and patterns in the regions where tumors are located for decision-making.<\/jats:p>","DOI":"10.3390\/info15040182","type":"journal-article","created":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T12:41:57Z","timestamp":1711543317000},"page":"182","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Bridging the Gap: Exploring Interpretability in Deep Learning Models for Brain Tumor Detection and Diagnosis from MRI Images"],"prefix":"10.3390","volume":"15","author":[{"given":"Wandile","family":"Nhlapho","sequence":"first","affiliation":[{"name":"Department of Mathematical and Computational Sciences, University of Venda, Thohoyandou 0950, South Africa"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9020-3885","authenticated-orcid":false,"given":"Marcellin","family":"Atemkeng","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Rhodes University, Grahamstown 6139, South Africa"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9975-8676","authenticated-orcid":false,"given":"Yusuf","family":"Brima","sequence":"additional","affiliation":[{"name":"Institute of Cognitive Science, Osnabr\u00fcck University, 49074 Osnabr\u00fcck, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean-Claude","family":"Ndogmo","sequence":"additional","affiliation":[{"name":"Department of Mathematical and Computational Sciences, University of Venda, Thohoyandou 0950, South Africa"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bau, D., Zhou, B., Khosla, A., Oliva, A., and Torralba, A. (2017, January 21\u201326). Network dissection: Quantifying interpretability of deep visual representations. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.354"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Abiwinanda, N., Hanif, M., Hesaputra, S.T., Handayani, A., and Mengko, T.R. (2018, January 3\u20138). Brain tumor classification using convolutional neural network. Proceedings of the World Congress on Medical Physics and Biomedical Engineering 2018, Prague, Czech Republic.","DOI":"10.1007\/978-981-10-9035-6_33"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Latif, G., Butt, M.M., Khan, A.H., Butt, O., and Iskandar, D.A. (2017, January 8\u201310). Multiclass brain Glioma tumor classification using block-based 3D Wavelet features of MR images. Proceedings of the 2017 4th International Conference on Electrical and Electronic Engineering (ICEEE), Ankara, Turkey.","DOI":"10.1109\/ICEEE2.2017.7935845"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/BF00344251","article-title":"Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position","volume":"36","author":"Fukushima","year":"1980","journal-title":"Biol. Cybern."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Brima, Y., and Atemkeng, M. (2022). Visual Interpretable and Explainable Deep Learning Models for Brain Tumor MRI and COVID-19 Chest X-ray Images. arXiv.","DOI":"10.21203\/rs.3.rs-3241888\/v1"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ebiele, J., Ansah-Narh, T., Djiokap, S., Proven-Adzri, E., and Atemkeng, M. (2020, January 14\u201316). Conventional machine learning based on feature engineering for detecting pneumonia from chest X-rays. Proceedings of the Conference of the South African Institute of Computer Scientists and Information Technologists 2020, Cape Town, South Africa.","DOI":"10.1145\/3410886.3410898"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Brima, Y., Atemkeng, M., Tankio Djiokap, S., Ebiele, J., and Tchakount\u00e9, F. (2021). Transfer learning for the detection and diagnosis of types of pneumonia including pneumonia induced by COVID-19 from chest X-ray images. Diagnostics, 11.","DOI":"10.20944\/preprints202107.0548.v1"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chattopadhay, A., Sarkar, A., Howlader, P., and Balasubramanian, V.N. (2018, January 12\u201315). Grad-CAM++: Generalized gradient-based visual explanations for deep convolutional networks. Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00097"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-CAM: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_10","unstructured":"Sundararajan, M., Taly, A., and Yan, Q. (2017, January 6\u201311). Axiomatic attribution for deep networks. Proceedings of the International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Dong, Z., Wu, L., Wang, S., and Zhou, Z. (2010, January 23\u201325). Feature extraction of brain MRI by stationary wavelet transform. Proceedings of the 2010 International Conference on Biomedical Engineering and Computer Science, Wuhan, China.","DOI":"10.1109\/ICBECS.2010.5462491"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1673","DOI":"10.1007\/s11548-022-02619-x","article-title":"Explainability of deep neural networks for MRI analysis of brain tumors","volume":"17","author":"Zeineldin","year":"2022","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1184","DOI":"10.2214\/AJR.18.20331","article-title":"What does deep learning see? Insights from a classifier trained to predict contrast enhancement phase from CT images","volume":"211","author":"Philbrick","year":"2018","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"103187","DOI":"10.1016\/j.nicl.2022.103187","article-title":"Detection of lesions in the optic nerve with magnetic resonance imaging using a 3D convolutional neural network","volume":"36","author":"Alberich","year":"2022","journal-title":"Neuroimage Clin."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zeiler, M., and Fergus, R. (2013). Visualizing and understanding convolutional networks. arXiv.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chatterjee, S., Das, A., Mandal, C., Mukhopadhyay, B., Vipinraj, M., Shukla, A., Nagaraja Rao, R., Sarasaen, C., Speck, O., and N\u00fcrnberger, A. (2022). TorchEsegeta: Framework for interpretability and explainability of image-based deep learning models. Appl. Sci., 12.","DOI":"10.20944\/preprints202201.0072.v1"},{"key":"ref_17","unstructured":"Springenberg, J., Dosovitskiy, A., Brox, T., and Riedmiller, M. (2014). Striving for simplicity: The all convolutional net. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"102391","DOI":"10.1016\/j.media.2022.102391","article-title":"Deep learning models for triaging hospital head MRI examinations","volume":"78","author":"Wood","year":"2022","journal-title":"Med. Image Anal."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Saleem, H., Shahid, A.R., and Raza, B. (2021). Visual interpretability in 3D brain tumor segmentation network. Comput. Biol. Med., 133.","DOI":"10.1016\/j.compbiomed.2021.104410"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Samek, W., Montavon, G., Vedaldi, A., Hansen, L., and M\u00fcller, K.R. (2019). Layer-wise relevance propagation: An overview In Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, Springer. Lecture notes in computer science.","DOI":"10.1007\/978-3-030-28954-6"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Golla, A.K., T\u00f6nnes, C., Russ, T., Bauer, D.F., Froelich, M.F., Diehl, S.J., Schoenberg, S.O., Keese, M., Schad, L.R., and Z\u00f6llner, F.G. (2021). Automated screening for abdominal aortic aneurysm in CT scans under clinical conditions using deep learning. Diagnostics, 11.","DOI":"10.3390\/diagnostics11112131"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2376","DOI":"10.1109\/JBHI.2021.3074893","article-title":"COVID-19 automatic diagnosis with radiographic imaging: Explainable attention transfer deep neural networks","volume":"25","author":"Shi","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"39757","DOI":"10.1109\/ACCESS.2021.3062493","article-title":"DeepKneeExplainer: Explainable knee osteoarthritis diagnosis from radiographs and magnetic resonance imaging","volume":"9","author":"Karim","year":"2021","journal-title":"IEEE Access."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"609468","DOI":"10.3389\/fnins.2020.609468","article-title":"Investigation of deep-learning-driven identification of multiple sclerosis patients based on susceptibility-weighted images using relevance analysis","volume":"14","author":"Lopatina","year":"2020","journal-title":"Front. Neurosci."},{"key":"ref_25","unstructured":"Shrikumar, A., Greenside, P., and Kundaje, A. (2017). Learning important features through propagating activation differences. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4573","DOI":"10.3390\/app11104573","article-title":"A review of explainable deep learning cancer detection models in medical imaging","volume":"11","author":"Gulum","year":"2021","journal-title":"Appl. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Singh, A., Sengupta, S., and Lakshminarayanan, V. (2020). Explainable deep learning models in medical image analysis. J. Imaging, 6.","DOI":"10.3390\/jimaging6060052"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"813072","DOI":"10.3389\/fimmu.2022.813072","article-title":"Predicting EGFR and PD-L1 status in NSCLC patients using multitask AI system based on CT images","volume":"13","author":"Wang","year":"2022","journal-title":"Front. Immunol."},{"key":"ref_29","first-page":"1","article-title":"Doctor\u2019s dilemma: Evaluating an explainable subtractive spatial lightweight convolutional neural network for brain tumor diagnosis","volume":"17","author":"Kumar","year":"2021","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1177\/15500594221122699","article-title":"A class activation map-based interpretable transfer learning model for automated detection of ADHD from fMRI data","volume":"54","author":"Uyulan","year":"2022","journal-title":"Clin. EEG Neurosci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3348","DOI":"10.1007\/s00330-019-06214-8","article-title":"Deep learning for liver tumor diagnosis part II: Convolutional neural network interpretation using radiologic imaging features","volume":"29","author":"Wang","year":"2019","journal-title":"Eur. Radiol."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Akatsuka, J., Yamamoto, Y., Sekine, T., Numata, Y., Morikawa, H., Tsutsumi, K., Yanagi, M., Endo, Y., Takeda, H., and Hayashi, T. (2019). Illuminating clues of cancer buried in prostate MR image: Deep learning and expert approaches. Biomolecules, 9.","DOI":"10.3390\/biom9110673"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/mp.15359","article-title":"A review of explainable and interpretable AI with applications in COVID-19 imaging","volume":"49","author":"Fuhrman","year":"2022","journal-title":"Med. Phys."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Alshazly, H., Linse, C., Barth, E., and Martinetz, T. (2021). Explainable COVID-19 detection using chest CT scans and deep learning. Sensors, 21.","DOI":"10.3390\/s21020455"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"781798","DOI":"10.3389\/fonc.2021.781798","article-title":"Automatic sequence-based network for lung diseases detection in chest CT","volume":"11","author":"Hao","year":"2021","journal-title":"Front. Oncol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.patrec.2021.08.035","article-title":"Deep transfer learning based classification model for COVID-19 using chest CT-scans","volume":"152","author":"Lahsaini","year":"2021","journal-title":"Pattern Recognit Lett."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"116540","DOI":"10.1016\/j.eswa.2022.116540","article-title":"Efficient and visualizable convolutional neural networks for COVID-19 classification using chest CT","volume":"195","author":"Garg","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"19246","DOI":"10.1007\/s11227-022-04631-z","article-title":"Explainable artificial intelligence approach in combating real-time surveillance of COVID19 pandemic from CT scan and X-ray images using ensemble model","volume":"78","author":"Ullah","year":"2022","journal-title":"J. Supercomput."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Lu, S.Y., Zhang, Z., Zhang, Y.D., and Wang, S.H. (2022). CGENet: A deep graph model for COVID-19 detection based on chest CT. Biology, 11.","DOI":"10.3390\/biology11010033"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1109\/TVCG.2021.3114851","article-title":"COVID-view: Diagnosis of COVID-19 using chest CT","volume":"28","author":"Jadhav","year":"2022","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"76","DOI":"10.14326\/abe.11.76","article-title":"A deep learning system to diagnose COVID-19 pneumonia using masked lung CT images to avoid AI-generated COVID-19 diagnoses that include data outside the lungs","volume":"11","author":"Nagaoka","year":"2022","journal-title":"Adv. Biomed. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Suri, J.S., Agarwal, S., Chabert, G.L., Carriero, A., Pasch\u00e8, A., Danna, P.S., Saba, L., Mehmedovi\u0107, A., Faa, G., and Singh, I.M. (2022). COVLIAS 20-cXAI: Cloud-based explainable deep learning system for COVID-19 lesion localization in computed tomography scans. Diagnostics, 12.","DOI":"10.3390\/diagnostics12061482"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"102114","DOI":"10.1016\/j.artmed.2021.102114","article-title":"An explainable AI system for automated COVID-19 assessment and lesion categorization from CT-scans","volume":"118","author":"Pennisi","year":"2021","journal-title":"Artif. Intell. Med."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Maaten, L.V.D., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Li, C.F., Xu, Y.D., Ding, X.H., Zhao, J.J., Du, R.Q., Wu, L.Z., and Sun, W.P. (2022). MultiR-net: A novel joint learning network for COVID-19 segmentation and classification. Comput. Biol. Med., 144.","DOI":"10.1016\/j.compbiomed.2022.105340"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1038\/s41598-021-04287-4","article-title":"Automated grading of enlarged perivascular spaces in clinical imaging data of an acute stroke cohort using an interpretable, 3D deep learning framework","volume":"12","author":"Williamson","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1007\/s40042-021-00202-2","article-title":"Cerebral hemorrhage detection and localization with medical imaging for cerebrovascular disease diagnosis and treatment using explainable deep learning","volume":"79","author":"Kim","year":"2021","journal-title":"J. Korean Phys. Soc."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"e014526","DOI":"10.1161\/CIRCIMAGING.122.014526","article-title":"Deep learning for explainable estimation of mortality risk from myocardial positron emission tomography images","volume":"15","author":"Singh","year":"2022","journal-title":"Circ. Cardiovasc. Imaging"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"154324","DOI":"10.1109\/ACCESS.2021.3127394","article-title":"A novel AI-based system for detection and severity prediction of dementia using MRI","volume":"9","author":"Jain","year":"2021","journal-title":"IEEE Access."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.schres.2021.06.011","article-title":"Structural and diffusion MRI based schizophrenia classification using 2D pretrained and 3D naive convolutional neural networks","volume":"243","author":"Hu","year":"2022","journal-title":"Schizophr. Res."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"5289","DOI":"10.1109\/JBHI.2021.3066832","article-title":"An explainable 3D residual self-attention deep neural network for joint atrophy localization and Alzheimer\u2019s disease diagnosis using structural MRI","volume":"26","author":"Zhang","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_52","unstructured":"Simonyan, K., Vedaldi, A., and Zisserman, A. (2013). Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv."},{"key":"ref_53","first-page":"1","article-title":"ImageNet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Proc. Adv. Neural Inf. Process. Syst."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Mascarenhas, S., and Agarwal, M. (2021, January 19\u201321). A comparison between VGG16, VGG19 and ResNet50 architecture frameworks for Image Classification. Proceedings of the 2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON), Bengaluru, India.","DOI":"10.1109\/CENTCON52345.2021.9687944"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2016, January 19\u201324). Xception: Deep Learning with Depthwise Separable Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_56","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 19\u201324). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_59","unstructured":"Tan, M., and Le, Q. (2019, January 9\u201315). Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the International Conference on Machine Learning, PMLR, Beach, CA, USA."},{"key":"ref_60","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16 \u00d7 16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"11440","DOI":"10.1038\/s41598-022-15634-4","article-title":"Vision Transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from CT-radiography","volume":"12","author":"Islam","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_62","unstructured":"Bhuvaji, S., Kadam, A., Bhumkar, P., and Dedge, S. (2023, July 20). Brain Tumor Classification (MRI) Kaggle Dataset. Available online: https:\/\/www.kaggle.com\/datasets\/sartajbhuvaji\/brain-tumor-classification-mri\/data."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/4\/182\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:19:36Z","timestamp":1760105976000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/4\/182"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,27]]},"references-count":62,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["info15040182"],"URL":"https:\/\/doi.org\/10.3390\/info15040182","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,27]]}}}