{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T09:02:53Z","timestamp":1787907773958,"version":"build-2784847793"},"reference-count":55,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,29]],"date-time":"2024-05-29T00:00:00Z","timestamp":1716940800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Technology Council, The Republic of China","award":["NSTC 112-2221-E-194-036"],"award-info":[{"award-number":["NSTC 112-2221-E-194-036"]}]},{"name":"National Science and Technology Council, The Republic of China","award":["112-2222-E-194-002"],"award-info":[{"award-number":["112-2222-E-194-002"]}]},{"name":"National Science and Technology Council, The Republic of China","award":["CYCH-CCU-2023-05"],"award-info":[{"award-number":["CYCH-CCU-2023-05"]}]},{"name":"National Science and Technology Council, The Republic of China","award":["KAFGH_D_113042"],"award-info":[{"award-number":["KAFGH_D_113042"]}]},{"name":"Ditmanson Medical Foundation of Chia-Yi Christian Hospital and the National Chung Cheng University Joint Research Program","award":["NSTC 112-2221-E-194-036"],"award-info":[{"award-number":["NSTC 112-2221-E-194-036"]}]},{"name":"Ditmanson Medical Foundation of Chia-Yi Christian Hospital and the National Chung Cheng University Joint Research Program","award":["112-2222-E-194-002"],"award-info":[{"award-number":["112-2222-E-194-002"]}]},{"name":"Ditmanson Medical Foundation of Chia-Yi Christian Hospital and the National Chung Cheng University Joint Research Program","award":["CYCH-CCU-2023-05"],"award-info":[{"award-number":["CYCH-CCU-2023-05"]}]},{"name":"Ditmanson Medical Foundation of Chia-Yi Christian Hospital and the National Chung Cheng University Joint Research Program","award":["KAFGH_D_113042"],"award-info":[{"award-number":["KAFGH_D_113042"]}]},{"DOI":"10.13039\/501100015045","name":"Kaohsiung Armed Forces General Hospital","doi-asserted-by":"publisher","award":["NSTC 112-2221-E-194-036"],"award-info":[{"award-number":["NSTC 112-2221-E-194-036"]}],"id":[{"id":"10.13039\/501100015045","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100015045","name":"Kaohsiung Armed Forces General Hospital","doi-asserted-by":"publisher","award":["112-2222-E-194-002"],"award-info":[{"award-number":["112-2222-E-194-002"]}],"id":[{"id":"10.13039\/501100015045","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100015045","name":"Kaohsiung Armed Forces General Hospital","doi-asserted-by":"publisher","award":["CYCH-CCU-2023-05"],"award-info":[{"award-number":["CYCH-CCU-2023-05"]}],"id":[{"id":"10.13039\/501100015045","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100015045","name":"Kaohsiung Armed Forces General Hospital","doi-asserted-by":"publisher","award":["KAFGH_D_113042"],"award-info":[{"award-number":["KAFGH_D_113042"]}],"id":[{"id":"10.13039\/501100015045","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>The early detection of esophageal cancer presents a substantial difficulty, which contributes to its status as a primary cause of cancer-related fatalities. This study used You Only Look Once (YOLO) frameworks, specifically YOLOv5 and YOLOv8, to predict and detect early-stage EC by using a dataset sourced from the Division of Gastroenterology and Hepatology, Ditmanson Medical Foundation, Chia-Yi Christian Hospital. The dataset comprised 2741 white-light images (WLI) and 2741 hyperspectral narrowband images (HSI-NBI). They were divided into 60% training, 20% validation, and 20% test sets to facilitate robust detection. The images were produced using a conversion method called the spectrum-aided vision enhancer (SAVE). This algorithm can transform a WLI into an NBI without requiring a spectrometer or spectral head. The main goal was to identify dysplasia and squamous cell carcinoma (SCC). The model\u2019s performance was evaluated using five essential metrics: precision, recall, F1-score, mAP, and the confusion matrix. The experimental results demonstrated that the HSI model exhibited improved learning capabilities for SCC characteristics compared with the original RGB images. Within the YOLO framework, YOLOv5 outperformed YOLOv8, indicating that YOLOv5\u2019s design possessed superior feature-learning skills. The YOLOv5 model, when used in conjunction with HSI-NBI, demonstrated the best performance. It achieved a precision rate of 85.1% (CI95: 83.2\u201387.0%, p &lt; 0.01) in diagnosing SCC and an F1-score of 52.5% (CI95: 50.1\u201354.9%, p &lt; 0.01) in detecting dysplasia. The results of these figures were much better than those of YOLOv8. YOLOv8 achieved a precision rate of 81.7% (CI95: 79.6\u201383.8%, p &lt; 0.01) and an F1-score of 49.4% (CI95: 47.0\u201351.8%, p &lt; 0.05). The YOLOv5 model with HSI demonstrated greater performance than other models in multiple scenarios. This difference was statistically significant, suggesting that the YOLOv5 model with HSI significantly improved detection capabilities.<\/jats:p>","DOI":"10.3390\/diagnostics14111129","type":"journal-article","created":{"date-parts":[[2024,5,29]],"date-time":"2024-05-29T06:58:07Z","timestamp":1716965887000},"page":"1129","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Evaluation of Spectrum-Aided Visual Enhancer (SAVE) in Esophageal Cancer Detection Using YOLO Frameworks"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2588-6883","authenticated-orcid":false,"given":"Chu-Kuang","family":"Chou","sequence":"first","affiliation":[{"name":"Division of Gastroenterology and Hepatology, Department of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chia-Yi 60002, Taiwan"},{"name":"Obesity Center, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chia-Yi 60002, Taiwan"},{"name":"Department of Medical Quality, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chia-Yi 60002, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Riya","family":"Karmakar","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, National Chung Cheng University, Chia-Yi 62102, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Ming","family":"Tsao","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, National Chung Cheng University, Chia-Yi 62102, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1802-3368","authenticated-orcid":false,"given":"Lim Wei","family":"Jie","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Multimedia University (Cyberjaya), Persiaran Multimedia, Cyberjaya 63100, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7741-3722","authenticated-orcid":false,"given":"Arvind","family":"Mukundan","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, National Chung Cheng University, Chia-Yi 62102, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chien-Wei","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Kaohsiung Armed Forces General Hospital, 2, Zhongzheng 1st. Rd., Lingya District, Kaohsiung City 80284, Taiwan"},{"name":"Department of Nursing, Tajen University, 20, Weixin Rd., Yanpu Township 90741, Pingtung County, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1542-1495","authenticated-orcid":false,"given":"Tsung-Hsien","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chia-Yi 60002, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chau-Yuan","family":"Ko","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Kaohsiung Armed Forces General Hospital, 2, Zhongzheng 1st. Rd., Lingya District, Kaohsiung City 80284, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4107-2062","authenticated-orcid":false,"given":"Hsiang-Chen","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, National Chung Cheng University, Chia-Yi 62102, Taiwan"},{"name":"Department of Medical Research, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, No. 2, Minsheng Road, Dalin, Chia-Yi 62247, Taiwan"},{"name":"Director of Technology Development, Hitspectra Intelligent Technology Co., Ltd., Kaohsiung City 80661, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/j.gtc.2016.04.001","article-title":"Esophageal cancer","volume":"45","author":"Alsop","year":"2016","journal-title":"Gastroenterol. Clin."},{"key":"ref_2","first-page":"22","article-title":"Esophageal cancer","volume":"95","author":"Short","year":"2017","journal-title":"Am. Fam. Physician"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yang, K.-Y., Fang, Y.-J., Karmakar, R., Mukundan, A., Tsao, Y.-M., Huang, C.-W., and Wang, H.-C. (2023). Assessment of narrow band imaging algorithm for video capsule endoscopy based on decorrelated color space for esophageal cancer. Cancers, 15.","DOI":"10.20944\/preprints202308.0280.v1"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1053\/j.gastro.2022.05.054","article-title":"The global landscape of esophageal squamous cell carcinoma and esophageal adenocarcinoma incidence and mortality in 2020 and projections to 2040: New estimates from GLOBOCAN 2020","volume":"163","author":"Morgan","year":"2022","journal-title":"Gastroenterology"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Rai, V., Abdo, J., and Agrawal, D.K. (2023). Biomarkers for Early Detection, Prognosis, and Therapeutics of Esophageal Cancers. Int. J. Mol. Sci., 24.","DOI":"10.3390\/ijms24043316"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1708","DOI":"10.3748\/wjg.v26.i15.1708","article-title":"Blood-based biomarkers for early detection of esophageal squamous cell carcinoma","volume":"26","author":"Chu","year":"2020","journal-title":"World J. Gastroenterol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"205","DOI":"10.4253\/wjge.v14.i4.205","article-title":"Role of endoscopic ultrasound in esophageal cancer","volume":"14","author":"Radlinski","year":"2022","journal-title":"World J. Gastrointest. Endosc."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"607","DOI":"10.3760\/cma.j.issn.0366-6999.20131691","article-title":"Integrated positron emission tomography and computed tomography in preoperative lymph node staging of non-small cell lung cancer","volume":"127","author":"Xu","year":"2014","journal-title":"Chin. Med. J."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Pellat, A., Dohan, A., Soyer, P., Veziant, J., Coriat, R., and Barret, M. (2022). The role of magnetic resonance imaging in the management of esophageal cancer. Cancers, 14.","DOI":"10.3390\/cancers14051141"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4061","DOI":"10.1364\/AO.53.004061","article-title":"Vascular contrast in narrow-band and white light imaging","volume":"53","author":"Wang","year":"2014","journal-title":"Appl. Opt."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Tsai, T.-J., Mukundan, A., Chi, Y.-S., Tsao, Y.-M., Wang, Y.-K., Chen, T.-H., Wu, I.-C., Huang, C.-W., and Wang, H.-C. (2022). Intelligent Identification of Early Esophageal Cancer by Band-Selective Hyperspectral Imaging. Cancers, 14.","DOI":"10.3390\/cancers14174292"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"0207017","DOI":"10.3788\/CJL201845.0207017","article-title":"Recent advances of hyperspectral imaging application in biomedicine","volume":"45","author":"Lixin","year":"2018","journal-title":"Chin. J. Lasers"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13206-021-00041-0","article-title":"Hyperspectral imaging for clinical applications","volume":"16","author":"Yoon","year":"2022","journal-title":"BioChip J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1265","DOI":"10.21037\/tcr.2019.12.53","article-title":"Applications of hyperspectral imaging in the detection and diagnosis of solid tumors","volume":"9","author":"Zhang","year":"2020","journal-title":"Transl. Cancer Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"417","DOI":"10.2174\/1573405618666220519144358","article-title":"Hyperspectral imaging: A review and trends towards medical imaging","volume":"19","author":"Karim","year":"2023","journal-title":"Curr. Med. Imaging"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1159\/000487470","article-title":"Narrow-band imaging: Clinical application in gastrointestinal endoscopy","volume":"26","author":"Barbeiro","year":"2018","journal-title":"GE Port. J. Gastroenterol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Mukundan, A., Hong-Thai, N., and Wang, H.-C. (September, January 31). Detection of PM 2.5 Particulates using a Snap-Shot Hyperspectral Imaging Technology. Proceedings of the Conference on Lasers and Electro-Optics\/Pacific Rim 2022, Sapporo, Japan.","DOI":"10.1109\/CLEO-PR62338.2022.10431996"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kefauver, S.C., Pe\u00f1uelas, J., and Ustin, S.L. (2012, January 22\u201327). Applications of hyperspectral remote sensing and GIS for assessing forest health and air pollution. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6350696"},{"key":"ref_19","first-page":"100580","article-title":"Deep learning ensemble method for classification of satellite hyperspectral images","volume":"23","author":"Iyer","year":"2021","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"7032","DOI":"10.1109\/JSTARS.2021.3090256","article-title":"Hyperspectral satellites, evolution, and development history","volume":"14","author":"Qian","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"113366","DOI":"10.1016\/j.rse.2022.113366","article-title":"Cross-scale sensing of field-level crop residue cover: Integrating field photos, airborne hyperspectral imaging, and satellite data","volume":"285","author":"Wang","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"031501","DOI":"10.1117\/1.JRS.15.031501","article-title":"Hyperspectral remote sensing in lithological mapping, mineral exploration, and environmental geology: An updated review","volume":"15","author":"Peyghambari","year":"2021","journal-title":"J. Appl. Remote Sens."},{"key":"ref_23","first-page":"879","article-title":"Hyperspectral remote sensing and geological applications","volume":"108","author":"Ramakrishnan","year":"2015","journal-title":"Curr. Sci."},{"key":"ref_24","unstructured":"Kurz, T., Buckley, S., and Becker, J. (2017, January 9\u201315). Hyperspectral imaging: A novel geological mapping technique for subsurface construction sites. Proceedings of the World Tunnel Congress 2017\u2014Surface Challenges\u2014Underground Solutions, Bergen, Norway."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Mukundan, A., Tsao, Y.-M., and Wang, H.-C. (2023, January 14\u201317). Detection of counterfeit holograms using hyperspectral imaging. Proceedings of the Holography, Diffractive Optics, and Applications XIII, Beijing, China.","DOI":"10.1117\/12.2688978"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1109\/MGRS.2019.2902525","article-title":"Hypersectral imaging for military and security applications: Combining myriad processing and sensing techniques","volume":"7","author":"Shimoni","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ke, C. (2017, January 15\u201317). Military object detection using multiple information extracted from hyperspectral imagery. Proceedings of the 2017 International Conference on Progress in Informatics and Computing (PIC), Nanjing, China.","DOI":"10.1109\/PIC.2017.8359527"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lu, B., Dao, P.D., Liu, J., He, Y., and Shang, J. (2020). Recent advances of hyperspectral imaging technology and applications in agriculture. Remote Sens., 12.","DOI":"10.3390\/rs12162659"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ad\u00e3o, T., Hru\u0161ka, J., P\u00e1dua, L., Bessa, J., Peres, E., Morais, R., and Sousa, J.J. (2017). Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sens., 9.","DOI":"10.3390\/rs9111110"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Tsai, C.-L., Mukundan, A., Chung, C.-S., Chen, Y.-H., Wang, Y.-K., Chen, T.-H., Tseng, Y.-S., Huang, C.-W., Wu, I.-C., and Wang, H.-C. (2021). Hyperspectral imaging combined with artificial intelligence in the early detection of esophageal cancer. Cancers, 13.","DOI":"10.3390\/cancers13184593"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Collins, T., Maktabi, M., Barberio, M., Bencteux, V., Jansen-Winkeln, B., Chalopin, C., Marescaux, J., Hostettler, A., Diana, M., and Gockel, I. (2021). Automatic recognition of colon and esophagogastric cancer with machine learning and hyperspectral imaging. Diagnostics, 11.","DOI":"10.3390\/diagnostics11101810"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Fang, Y.-J., Mukundan, A., Tsao, Y.-M., Huang, C.-W., and Wang, H.-C. (2022). Identification of early esophageal cancer by semantic segmentation. J. Pers. Med., 12.","DOI":"10.3390\/jpm12081204"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4508","DOI":"10.1038\/s41598-022-07524-6","article-title":"Tumor cell identification and classification in esophageal adenocarcinoma specimens by hyperspectral imaging","volume":"12","author":"Maktabi","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"13797","DOI":"10.1038\/s41598-018-32139-1","article-title":"Early identification of esophageal squamous neoplasm by hyperspectral endoscopic imaging","volume":"8","author":"Wu","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"766243","DOI":"10.3389\/fonc.2022.766243","article-title":"Development of a Deep Learning System to Detect Esophageal Cancer by Barium Esophagram","volume":"12","author":"Zhang","year":"2022","journal-title":"Front. Oncol."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Qureshi, R., Ragab, M.G., Abdulkader, S.J., Alqushaib, A., Sumiea, E.H., and Alhussian, H. (2023). A Comprehensive Systematic Review of YOLO for Medical Object Detection (2018 to 2023). Authorea Prepr.","DOI":"10.36227\/techrxiv.23681679.v1"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s11548-023-02944-9","article-title":"A YOLOv5-based network for the detection of a diffuse reflectance spectroscopy probe to aid surgical guidance in gastrointestinal cancer surgery","volume":"19","author":"Gkouzionis","year":"2024","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1016\/j.procs.2023.03.083","article-title":"Skin Cancer diagnosis with Yolo Deep Neural Network","volume":"220","author":"Aishwarya","year":"2023","journal-title":"Procedia Comput. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1142\/S2196888823500094","article-title":"Anomalies Detection in Chest X-Rays Images Using Faster R-CNN and YOLO","volume":"10","author":"Nguyen","year":"2023","journal-title":"Vietnam. J. Comput. Sci. (World Sci.)"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s12559-023-10189-6","article-title":"A YOLO-based model for breast cancer detection in mammograms","volume":"16","author":"Prinzi","year":"2024","journal-title":"Cogn. Comput."},{"key":"ref_41","first-page":"892","article-title":"Enhancing COVID-19 safety: Exploring yolov8 object detection for accurate face mask classification","volume":"11","author":"Tamang","year":"2023","journal-title":"Int. J. Intell. Syst. Appl. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"5483","DOI":"10.3748\/wjg.v28.i37.5483","article-title":"Application of an artificial intelligence system for endoscopic diagnosis of superficial esophageal squamous cell carcinoma","volume":"28","author":"Meng","year":"2022","journal-title":"World J. Gastroenterol."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Wang, F., and Su, J. (2021, January 18\u201320). Based on the improved YOLOV3 small target detection algorithm. Proceedings of the 2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Chongqing, China.","DOI":"10.1109\/IMCEC51613.2021.9482076"},{"key":"ref_44","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_45","unstructured":"Thuan, D. (2021). Evolution of Yolo Algorithm and Yolov5: The State-of-the-Art Object Detention Algorithm. [Bachelor\u2019s Thesis, Oulu University of Applied Sciences]."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_47","unstructured":"Mehra, A. (2024, May 15). Understanding YOLOv8 Architecture, Applications & Features. Labellerr. Available online: https:\/\/www.labellerr.com\/blog\/understanding-yolov8-architecture-applications-features\/."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Sohan, M., Sai Ram, T., Reddy, R., and Venkata, C. (2024, January 27\u201328). A Review on YOLOv8 and Its Advancements. Proceedings of the International Conference on Data Intelligence and Cognitive Informatics, Tirunelveli, India.","DOI":"10.1007\/978-981-99-7962-2_39"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Adjobo, E.C., Mahama, A.T.S., Gouton, P., and Tossa, J. (2023). Automatic Localization of Five Relevant Dermoscopic Structures Based on YOLOv8 for Diagnosis Improvement. J. Imaging, 9.","DOI":"10.3390\/jimaging9070148"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1080\/00325481.2021.1934495","article-title":"Recent advances in early esophageal cancer: Diagnosis and treatment based on endoscopy","volume":"133","author":"Yang","year":"2021","journal-title":"Postgrad. Med."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Jansen, M., and Wright, N.A. (2016). Stem Cells, Pre-Neoplasia, and Early Cancer of the Upper Gastrointestinal Tract, Springer.","DOI":"10.1007\/978-3-319-41388-4"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Dumoulin, F.L., Hildenbrand, R., Oyama, T., and Steinbr\u00fcck, I. (2021). Current trends in endoscopic diagnosis and treatment of early esophageal cancer. Cancers, 13.","DOI":"10.3390\/cancers13040752"},{"key":"ref_53","unstructured":"SeekFire (2024, May 15). Overview of Model Structure About YOLOv5 \u00b7 Issue #280 \u00b7 ultralytics\/yolov5. Available online: https:\/\/github.com\/ultralytics\/yolov5\/issues\/280."},{"key":"ref_54","unstructured":"King, R. (2024, May 15). Brief Summary of YOLOv8 Model Structure \u00b7 Issue #189 \u00b7 ultralytics\/ultralytics. Available online: https:\/\/github.com\/ultralytics\/ultralytics\/issues\/189."},{"key":"ref_55","unstructured":"Khandelwal, R. (2024, May 15). Evaluating Performance of an Object Detection Model. Available online: https:\/\/towardsdatascience.com\/evaluating-performance-of-an-object-detection-model-137a349c517b."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/11\/1129\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:50:13Z","timestamp":1760107813000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/11\/1129"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,29]]},"references-count":55,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["diagnostics14111129"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics14111129","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,29]]}}}