{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T12:10:01Z","timestamp":1793275801402,"version":"4.5.0"},"reference-count":86,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T00:00:00Z","timestamp":1733702400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: The growing demand for artificial intelligence (AI) in healthcare is driven by the need for more robust and automated diagnostic systems. These methods not only provide accurate diagnoses but also promise to enhance operational efficiency and optimize resource utilization in clinical workflows. In the field of dental lesion detection, the application of deep learning models to various imaging techniques has gained significant prominence. This study presents a comprehensive systematic review of the utilization of deep learning methods for detecting dental lesions across different imaging modalities, including panoramic imaging, periapical radiographs, and cone-beam computed tomography (CBCT). A systematic search was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure a structured and transparent review process. Methods: This study addresses four key research questions related to the types of objects used for AI in dental images, state-of-the-art approaches for detecting lesions in dental images, data augmentation methods, and challenges and possible solutions to the existing AI-based dental lesion detection. Furthermore, this systematic review was performed on 29 primary studies identified from multiple electronic databases. This review focused on studies published between 2019 and 2024, sourced from IEEE, Web of Knowledge, Springer, ScienceDirect, PubMed, and Google Scholar. Results: We identified five types of lesions in dental images as periapical lesions, cyst lesions, jawbone lesions, dental caries, and apical lesions. Among the fourteen state-of-the-art deep learning approaches, the results demonstrate that deep learning models, such as U-Net, AlexNet, and You Only Look Once (YOLO) version 8 (YOLOv8) are commonly employed for dental lesion detection. These deep learning models have the potential to serve as integral components of decision-making processes by improving detection accuracy and supporting clinical workflows. Furthermore, we found that among twelve types of data augmentation techniques, flipping, rotation, and reflection methods played an important role in increasing the diversity of the datasets. We also identified six challenges for dental lesion detection, and the main issues were identified as data integration, poor data quality, limited model generalization, and overfitting. Proposed solutions against the aforementioned challenges include the integration of larger datasets, model optimization, and diversification of data sources. Conclusions: This study provides a comprehensive overview of current methodologies and potential advancements in dental lesion detection using deep learning. The findings indicate that possible solutions against the challenges of AI-based diagnostic methods in dental lesion detection need to be more generalizable regardless of image type, the number of data, and data quality.<\/jats:p>","DOI":"10.3390\/diagnostics14232768","type":"journal-article","created":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T11:16:48Z","timestamp":1733743008000},"page":"2768","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Comprehensive Insights into Artificial Intelligence for Dental Lesion Detection: A Systematic Review"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-1099-6985","authenticated-orcid":false,"given":"Kubra","family":"Demir","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Erzurum Technical University, 25040 Erzurum, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8765-0038","authenticated-orcid":false,"given":"Ozlem","family":"Sokmen","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Erzurum Technical University, 25040 Erzurum, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4156-9098","authenticated-orcid":false,"given":"Isil","family":"Karabey Aksakalli","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Erzurum Technical University, 25040 Erzurum, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7200-3436","authenticated-orcid":false,"given":"Kubra","family":"Torenek-Agirman","sequence":"additional","affiliation":[{"name":"Department of Dentomaxillofacial Radiology, Ataturk University, 25240 Erzurum, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,9]]},"reference":[{"key":"ref_1","first-page":"165","article-title":"Radiographical approach to jaw lesions","volume":"49","author":"Neyaz","year":"2008","journal-title":"Singap. Med. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"475","DOI":"10.5152\/dir.2014.14143","article-title":"Nonodontogenic mandibular lesions: Differentiation based on CT attenuation","volume":"20","author":"Kara","year":"2014","journal-title":"Diagn. Interv. Radiol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"AbuSalim, S., Zakaria, N., Islam, M.R., Kumar, G., Mokhtar, N., and Abdulkadir, S.J. (2022). Analysis of deep learning techniques for dental informatics: A systematic literature review. Healthcare, 10.","DOI":"10.3390\/healthcare10101892"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"N.71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: An updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref_5","unstructured":"Kitchenham, B., and Charters, S. (2024, December 03). Guidelines for Performing Systematic Literature Reviews in Software Engineering. Available online: https:\/\/www.researchgate.net\/publication\/302924724_Guidelines_for_performing_Systematic_Literature_Reviews_in_Software_Engineering."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wohlin, C. (2014, January 13\u201314). Guidelines for snowballing in systematic literature studies and a replication in software engineering. Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering, London, UK.","DOI":"10.1145\/2601248.2601268"},{"key":"ref_7","first-page":"21","article-title":"Applying the Goal\/Question\/Metric paradigm in the experience factory","volume":"7","author":"Basili","year":"1993","journal-title":"Softw. Qual. Assur. Meas. A Worldw. Perspect."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kitchenham, B.A., Budgen, D., and Brereton, P. (2015). Evidence-Based Software Engineering and Systematic Reviews, CRC Press.","DOI":"10.1201\/b19467"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Liberati, A., Altman, D.G., Tetzlaff, J., Mulrow, C., G\u00f8tzsche, P.C., Ioannidis, J.P., Clarke, M., Devereaux, P.J., Kleijnen, J., and Moher, D. (2009). The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: Explanation and elaboration. Ann. Intern. Med., 151.","DOI":"10.2427\/5768"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"264","DOI":"10.7326\/0003-4819-151-4-200908180-00135","article-title":"Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement","volume":"151","author":"Moher","year":"2009","journal-title":"Ann. Intern. Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1007\/s00784-021-04043-y","article-title":"Deep learning for categorization of endodontic lesion based on radiographic periapical index scoring system","volume":"26","author":"Moidu","year":"2022","journal-title":"Clin. Oral Investig."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1007\/s11282-020-00485-4","article-title":"Deep learning object detection of maxillary cyst-like lesions on panoramic radiographs: Preliminary study","volume":"37","author":"Watanabe","year":"2021","journal-title":"Oral Radiol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Gwak, M., Yun, J.P., Lee, J.Y., Han, S.S., Park, P., and Lee, C. (2024). Attention-guided jaw bone lesion diagnosis in panoramic radiography using minimal labeling effort. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-55677-3"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"El Bagoury, M., Al-Shetairy, M., Ahmed, O., Mehanny, S.S., Hamdy, T., Omar, G., Akram, M., Mohamed, R., Amr, H., and Gad, W. (2023, January 21\u201323). Dental Disease Detection based on CNN for Panoramic Dental Radiographs. Proceedings of the 2023 Eleventh International Conference on Intelligent Computing and Information Systems (ICICIS), Cairo, Egypt.","DOI":"10.1109\/ICICIS58388.2023.10391205"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kaarthik, K., Vivek, G., Dineshkumar, K., and Balasubramani, A. (2022, January 25\u201327). Detection and Classification of Dental Defect using CNN. Proceedings of the 2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India.","DOI":"10.1109\/ICICCS53718.2022.9788294"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"118654","DOI":"10.1109\/ACCESS.2022.3220335","article-title":"Missing teeth and restoration detection using dental panoramic radiography based on transfer learning with CNNs","volume":"10","author":"Chen","year":"2022","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Demir, K., Aksakalli, I.K., Bay\u011fin, N., and S\u00f6kmen, \u00d6.\u00c7. (2023, January 11\u201313). Deep Learning Based Lesion Detection on Dental Panoramic Radiographs. Proceedings of the 2023 Innovations in Intelligent Systems and Applications Conference (ASYU), Sivas, Turkiye.","DOI":"10.1109\/ASYU58738.2023.10296824"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Li, C.W., Lin, S.Y., Chou, H.S., Chen, T.Y., Chen, Y.A., Liu, S.Y., Liu, Y.L., Chen, C.A., Huang, Y.C., and Chen, S.L. (2021). Detection of dental apical lesions using CNNs on periapical radiograph. Sensors, 21.","DOI":"10.3390\/s21217049"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chuo, Y., Lin, W.M., Chen, T.Y., Chan, M.L., Chang, Y.S., Lin, Y.R., Lin, Y.J., Shao, Y.H., Chen, C.A., and Chen, S.L. (2022). A high-accuracy detection system: Based on transfer learning for apical lesions on periapical radiograph. Bioengineering, 9.","DOI":"10.3390\/bioengineering9120777"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"351","DOI":"10.5624\/isd.20220078","article-title":"Deep learning-based apical lesion segmentation from panoramic radiographs","volume":"52","author":"Song","year":"2022","journal-title":"Imaging Sci. Dent."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"20220122","DOI":"10.1259\/dmfr.20220122","article-title":"The effect of a deep-learning tool on dentists\u2019 performances in detecting apical radiolucencies on periapical radiographs","volume":"51","author":"Hamdan","year":"2022","journal-title":"Dentomaxillofac. Radiol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.4103\/njcp.njcp_624_22","article-title":"Evaluation of an Artificial Intelligence System for the Diagnosis of Apical Periodontitis on Digital Panoramic Images","volume":"26","author":"Karakurt","year":"2023","journal-title":"Niger. J. Clin. Pract."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ba-Hattab, R., Barhom, N., Osman, S.A.A., Naceur, I., Odeh, A., Asad, A., Al-Najdi, S.A.R., Ameri, E., Daer, A., and Silva, R.L.D. (2023). Detection of periapical lesions on panoramic radiographs using deep learning. Appl. Sci., 13.","DOI":"10.3390\/app13031516"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1016\/j.joen.2020.03.025","article-title":"Artificial intelligence for the computer-aided detection of periapical lesions in cone-beam computed tomographic images","volume":"46","author":"Setzer","year":"2020","journal-title":"J. Endod."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Hadzic, A., Urschler, M., Press, J.N.A., Riedl, R., Rugani, P., \u0160tern, D., and Kirnbauer, B. (2023). Evaluating a Periapical Lesion Detection CNN on a Clinically Representative CBCT Dataset\u2014A Validation Study. J. Clin. Med., 13.","DOI":"10.3390\/jcm13010197"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Krois, J., Garcia Cantu, A., Chaurasia, A., Patil, R., Chaudhari, P.K., Gaudin, R., Gehrung, S., and Schwendicke, F. (2021). Generalizability of deep learning models for dental image analysis. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-85454-5"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"20230118","DOI":"10.1259\/dmfr.20230118","article-title":"The role of deep learning for periapical lesion detection on panoramic radiographs","volume":"52","author":"Kaya","year":"2023","journal-title":"Dentomaxillofac. Radiol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"105016","DOI":"10.1016\/j.imavis.2024.105016","article-title":"Detection of dental periapical lesions using retinex based image enhancement and lightweight deep learning model","volume":"146","author":"Latke","year":"2024","journal-title":"Image Vis. Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.oooo.2023.11.006","article-title":"Multi-model deep learning approach for segmentation of teeth and periapical lesions on pantomographs","volume":"138","author":"Adnan","year":"2024","journal-title":"Oral Surg. Oral Med. Oral Pathol. Oral Radiol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1016\/j.sdentj.2022.06.005","article-title":"Apical status and prevalence of endodontic treated teeth among Saudi adults in Eastern province: A prospective radiographic evaluation","volume":"34","author":"Altaroti","year":"2022","journal-title":"Saudi Dent. J."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1016\/j.joen.2019.03.016","article-title":"Deep learning for the radiographic detection of apical lesions","volume":"45","author":"Ekert","year":"2019","journal-title":"J. Endod."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1434","DOI":"10.1016\/j.joen.2022.07.013","article-title":"Automatic detection of periapical osteolytic lesions on cone-beam computed tomography using deep convolutional neuronal networks","volume":"48","author":"Kirnbauer","year":"2022","journal-title":"J. Endod."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Bayrakdar, I.S., Orhan, K., \u00c7elik, \u00d6., Bilgir, E., Sa\u011flam, H., Kaplan, F.A., G\u00f6r\u00fcr, S.A., Odaba\u015f, A., Aslan, A.F., and R\u00f3\u017cy\u0142o-Kalinowska, I. (2022). AU-net approach to apical lesion segmentation on panoramic radiographs. BioMed Res. Int., 2022.","DOI":"10.1155\/2022\/7035367"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Endres, M.G., Hillen, F., Salloumis, M., Sedaghat, A.R., Niehues, S.M., Quatela, O., Hanken, H., Smeets, R., Beck-Broichsitter, B., and Rendenbach, C. (2020). Development of a deep learning algorithm for periapical disease detection in dental radiographs. Diagnostics, 10.","DOI":"10.3390\/diagnostics10060430"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"104581","DOI":"10.1016\/j.jdent.2023.104581","article-title":"A deep learning approach for radiological detection and classification of radicular cysts and periapical granulomas","volume":"135","author":"Saadi","year":"2023","journal-title":"J. Dent."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Calazans, M.A.A., Ferreira, F.A.B., Alcoforado, M.d.L.M.G., Santos, A.d., Pontual, A.d.A., and Madeiro, F. (2022). Automatic classification system for periapical lesions in cone-beam computed tomography. Sensors, 22.","DOI":"10.3390\/s22176481"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Sajad, M., Shafi, I., and Ahmad, J. (2019, January 24\u201325). Automatic lesion detection in periapical X-Rays. Proceedings of the 2019 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), Swat, Pakistan.","DOI":"10.1109\/ICECCE47252.2019.8940661"},{"key":"ref_38","first-page":"190","article-title":"Periapical lesion diagnosis support system based on X-Ray images using machine learning technique","volume":"12","author":"Ngoc","year":"2021","journal-title":"World"},{"key":"ref_39","first-page":"396","article-title":"A New Approach towards Detection of Periapical Lesions using Artificial Intelligence","volume":"9","author":"Latke","year":"2023","journal-title":"Grenze Int. J. Eng. Technol. (GIJET)"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.infsof.2008.09.009","article-title":"Systematic literature reviews in software engineering\u2013a systematic literature review","volume":"51","author":"Kitchenham","year":"2009","journal-title":"Inf. Softw. Technol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1111\/iej.13256","article-title":"Prevalence of apical periodontitis and its association with previous root canal treatment, root canal filling length and type of coronal restoration\u2013a cross-sectional study","volume":"53","author":"Meirinhos","year":"2020","journal-title":"Int. Endod. J."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Titinchi, F., and Morkel, J. (2020). Residual cyst of the jaws: A clinico-pathologic study of this seemingly inconspicuous lesion. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0244250"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"20200185","DOI":"10.1259\/dmfr.20200185","article-title":"Automatic diagnosis for cysts and tumors of both jaws on panoramic radiographs using a deep convolution neural network","volume":"49","author":"Kwon","year":"2020","journal-title":"Dentomaxillofac. Radiol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"240","DOI":"10.38053\/acmj.1472247","article-title":"Dental caries from the past to the future: Is it possible to reduce caries prevalence?","volume":"6","author":"Kara","year":"2024","journal-title":"Anatol. Curr. Med. J."},{"key":"ref_45","unstructured":"Blake, A., Tuttle, T., and McKinney, R. (2023). Apical Periodontitis, StatPearls Publishing."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Chen, Z., Chen, S., and Hu, F. (2023). CTA-UNet: CNN-transformer architecture UNet for dental CBCT images segmentation. Phys. Med. Biol., 68.","DOI":"10.1088\/1361-6560\/acf026"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"7882924","DOI":"10.1155\/2022\/7882924","article-title":"Efficient 3D AlexNet architecture for object recognition using syntactic patterns from medical images","volume":"2022","author":"Rani","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_48","first-page":"30","article-title":"Di\u015f g\u00f6r\u00fcnt\u00fcleri \u00fczerinde g\u00f6r\u00fcnt\u00fc i\u015fleme ve derin \u00f6\u011frenme y\u00f6ntemleri kullan\u0131larak \u00e7\u00fcr\u00fck seviyesinin s\u0131n\u0131fland\u0131r\u0131lmas\u0131","volume":"2","author":"Adem","year":"2022","journal-title":"Uluslararas\u0131 Sivas Bilim Teknol. \u00dcniversitesi Derg."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1066","DOI":"10.1016\/j.procs.2022.01.135","article-title":"A Review of Yolo algorithm developments","volume":"199","author":"Jiang","year":"2022","journal-title":"Procedia Comput. Sci."},{"key":"ref_50","first-page":"335","article-title":"The Deep Learning Model for Decayed-Missing-Filled Teeth Detection: A Comparison Between YOLOV5 and YOLOV8","volume":"10","author":"Fitria","year":"2024","journal-title":"Jordanian J. Comput. Inf. Technol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1396","DOI":"10.1109\/TVCG.2020.3030418","article-title":"CNN explainer: Learning convolutional neural networks with interactive visualization","volume":"27","author":"Wang","year":"2020","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"5455","DOI":"10.1007\/s10462-020-09825-6","article-title":"A survey of the recent architectures of deep convolutional neural networks","volume":"53","author":"Khan","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"68547","DOI":"10.1007\/s11042-024-19154-3","article-title":"A novel teeth segmentation on three-dimensional dental model using adaptive enhanced googlenet classifier","volume":"83","author":"Hameed","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.neucom.2016.11.023","article-title":"G-MS2F: GoogLeNet based multi-stage feature fusion of deep CNN for scene recognition","volume":"225","author":"Tang","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Verdhan, V., and Verdhan, V. (2021). VGGNet and AlexNet networks. Computer Vision Using Deep Learning: Neural Network Architectures with Python and Keras, Apress.","DOI":"10.1007\/978-1-4842-6616-8"},{"key":"ref_56","unstructured":"Lindberg, A., and Larsson, L. (2024, December 03). Evaluating the Performance of Extended Convolutional Networks: A Comparative Study between VGG-16 and VGG-23 for Image Classification. Available online: https:\/\/kth.diva-portal.org\/smash\/get\/diva2:1886717\/FULLTEXT01.pdf."},{"key":"ref_57","first-page":"23","article-title":"Transfer learning for multi-crop leaf disease image classification using convolutional neural network VGG","volume":"6","author":"Paymode","year":"2022","journal-title":"Artif. Intell. Agric."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"8253","DOI":"10.1007\/s00521-022-06918-x","article-title":"VGGCOV19-NET: Automatic detection of COVID-19 cases from X-Ray images using modified VGG19 CNN architecture and YOLO algorithm","volume":"34","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Awan, M.J., Masood, O.A., Mohammed, M.A., Yasin, A., Zain, A.M., Dama\u0161evi\u010dius, R., and Abdulkareem, K.H. (2021). Image-based malware classification using VGG19 network and spatial convolutional attention. Electronics, 10.","DOI":"10.3390\/electronics10192444"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Nife, N.I., and Chtourou, M. (2023). A Comprehensive Study of Deep Learning and Performance Comparison of Deep Neural Network Models (YOLO, RetinaNet). Int. J. Online Biomed. Eng., 19.","DOI":"10.3991\/ijoe.v19i12.42607"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.patrec.2022.01.021","article-title":"Iou-balanced loss functions for single-stage object detection","volume":"156","author":"Wu","year":"2022","journal-title":"Pattern Recognit. Lett."},{"key":"ref_62","unstructured":"Zhang, H., Chang, H., Ma, B., Shan, S., and Chen, X. (2019). Cascade RetinaNet: Maintaining consistency for single-stage object detection. arXiv."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/TPAMI.2019.2938758","article-title":"Res2net: A new multi-scale backbone architecture","volume":"43","author":"Gao","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Koonce, B., and Koonce, B.E. (2021). Convolutional Neural Networks with Swift for Tensorflow: Image Recognition and Dataset Categorization, Springer.","DOI":"10.1007\/978-1-4842-6168-2"},{"key":"ref_65","unstructured":"K\u00f6m\u00fcrc\u00fc, E.Y. (2024, December 03). Methods for Developing Tiny Convolutional Neural Networks for Deployment on Embedded Systems. Available online: https:\/\/uu.diva-portal.org\/smash\/get\/diva2:1809980\/FULLTEXT01.pdf."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Setiawan, W., Ghofur, A., Rachman, F.H., and Rulaningtyas, R. (2021). Deep convolutional neural network alexnet and squeezenet for maize leaf diseases image classification. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Available online: https:\/\/kinetik.umm.ac.id\/index.php\/kinetik\/article\/view\/1335.","DOI":"10.22219\/kinetik.v6i4.1335"},{"key":"ref_67","unstructured":"Boesch, G. (2024, June 03). Segment Anything Model (SAM)\u2014The Complete 2025 Guide. Available online: https:\/\/viso.ai\/deep-learning\/segment-anything-model-sam-explained\/."},{"key":"ref_68","unstructured":"Mandal, B., Okeukwu, A., and Theis, Y. (2021). Masked face recognition using resnet-50. arXiv."},{"key":"ref_69","first-page":"56","article-title":"Gradient-Watershed Transform Segmentation and ResNet50-Based Classification and Detection of Tumor from MRI Images","volume":"26","author":"Mantri","year":"2024","journal-title":"Stoch. Model."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Byzkrovnyi, O., Smelyakov, K., Chupryna, A., and Lanovyy, O. (2024, January 25). Comparison of Object Detection Algorithms for the Task of Person Detection on Jetson TX2 NX Platform. Proceedings of the 2024 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream), Vilnius, Lithuania.","DOI":"10.1109\/eStream61684.2024.10542592"},{"key":"ref_71","unstructured":"Rajpura, P.S., Bojinov, H., and Hegde, R.S. (2017). Object detection using deep cnns trained on synthetic images. arXiv."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Dong, K., Zhou, C., Ruan, Y., and Li, Y. (2020, January 18\u201320). MobileNetV2 model for image classification. Proceedings of the 2020 2nd International Conference on Information Technology and Computer Application (ITCA), Guangzhou, China.","DOI":"10.1109\/ITCA52113.2020.00106"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Tarek, H., Aly, H., Eisa, S., and Abul-Soud, M. (2022). Optimized deep learning algorithms for tomato leaf disease detection with hardware deployment. Electronics, 11.","DOI":"10.3390\/electronics11010140"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.neunet.2019.06.006","article-title":"Shunt connection: An intelligent skipping of contiguous blocks for optimizing MobileNet-V2","volume":"118","author":"Singh","year":"2019","journal-title":"Neural Netw."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on image data augmentation for deep learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1487","DOI":"10.1016\/j.engappai.2012.11.011","article-title":"Image contrast enhancement for preserving mean brightness without losing image features","volume":"26","author":"Huang","year":"2013","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Anwar, T., and Zakir, S. (2021, January 5\u20137). Effect of image augmentation on ECG image classification using deep learning. Proceedings of the 2021 International Conference on Artificial Intelligence (ICAI), Islamabad, Pakistan.","DOI":"10.1109\/ICAI52203.2021.9445258"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1007\/s00778-022-00775-9","article-title":"Data collection and quality challenges in deep learning: A data-centric ai perspective","volume":"32","author":"Whang","year":"2023","journal-title":"VLDB J."},{"key":"ref_79","first-page":"31","article-title":"Removing the noise from X-Ray image using Image processing Technology: A Bibliometric Survey and Future Research Directions","volume":"5","author":"Kamboj","year":"2021","journal-title":"Libr. Philos. Pract."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Yoon, J.S., Oh, K., Shin, Y., Mazurowski, M.A., and Suk, H.I. (2023). Domain generalization for medical image analysis: A survey. arXiv.","DOI":"10.1109\/JPROC.2024.3507831"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Mure\u0219anu, S., Hede\u0219iu, M., Iacob, L., Eftimie, R., Olariu, E., Dinu, C., Jacobs, R., and Group, T.P. (2024). Automating Dental Condition Detection on Panoramic Radiographs: Challenges, Pitfalls, and Opportunities. Diagnostics, 14.","DOI":"10.3390\/diagnostics14202336"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"2585","DOI":"10.1007\/s10115-021-01605-0","article-title":"Model complexity of deep learning: A survey","volume":"63","author":"Hu","year":"2021","journal-title":"Knowl. Inf. Syst."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Prajapati, S.A., Nagaraj, R., and Mitra, S. (2017, January 11\u201314). Classification of dental diseases using CNN and transfer learning. Proceedings of the 2017 5th International Symposium on Computational and Business Intelligence (ISCBI), Dubai, United Arab Emirates.","DOI":"10.1109\/ISCBI.2017.8053547"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.joen.2022.12.007","article-title":"Deep learning for detection of periapical radiolucent lesions: A systematic review and meta-analysis of diagnostic test accuracy","volume":"49","author":"Sadr","year":"2023","journal-title":"J. Endod."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"10227","DOI":"10.1109\/TGRS.2020.3042974","article-title":"PSGAN: A generative adversarial network for remote sensing image pan-sharpening","volume":"59","author":"Liu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.infsof.2015.03.007","article-title":"Guidelines for conducting systematic mapping studies in software engineering: An update","volume":"64","author":"Petersen","year":"2015","journal-title":"Inf. Softw. Technol."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/23\/2768\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:50:48Z","timestamp":1760115048000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/23\/2768"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,9]]},"references-count":86,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["diagnostics14232768"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics14232768","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,9]]}}}