{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T19:54:22Z","timestamp":1792180462069,"version":"4.4.0"},"reference-count":55,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T00:00:00Z","timestamp":1781568000000},"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":["113-2221-E-194-011-MY3"],"award-info":[{"award-number":["113-2221-E-194-011-MY3"]}]},{"DOI":"10.13039\/501100012537","name":"Ditmanson Medical Foundation, Chia-Yi Christian Hospital-National Chung Cheng University Joint Research Program","doi-asserted-by":"publisher","award":["CYCH-CCU-2025-06"],"award-info":[{"award-number":["CYCH-CCU-2025-06"]}],"id":[{"id":"10.13039\/501100012537","id-type":"DOI","asserted-by":"publisher"}]},{"award":["CYCH-CCU-2025-06"],"award-info":[{"award-number":["CYCH-CCU-2025-06"]}],"id":[{"id":"https:\/\/ror.org\/0028v3876","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100015045","name":"aohsiung Armed Forces General Hospital Research Program","doi-asserted-by":"publisher","award":["KAFGH_D_115059"],"award-info":[{"award-number":["KAFGH_D_115059"]}],"id":[{"id":"10.13039\/501100015045","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: The early identification of skin cancer by standard RGB dermoscopy is a clinical difficulty because of the complex visual differences between impacted lesions and healthy tissue. Methods: For the biomedical challenge, a novel approach to signal processing and image reconstruction is introduced in this study, called the spectrum-aided visual enhancer (SAVE). The proposed SAVE mechanism aims at reconstructing the diagnostically relevant spectral information from the conventional RGB dermoscopic images using the principles of hyperspectral imaging (HSI) and band selection (BS). After quality control and pre-processing, the images in the ISIC2019 dataset were selected, with 865 images that contain basal cell carcinoma (BCC), seborrheic keratosis (SK), and actinic keratosis (AK) lesions. To reduce data leakage, the dataset was split into training, validation, and testing subsets of 70%, 20%, and 10%, respectively. Five supervised deep learning object detection models were trained and tested on the conventional RGB image dataset and on the SAVE-enhanced dataset. Five supervised deep learning object detection models, namely, YOLOv8, YOLOv10, YOLOv11, SSDLite, and SSD, were trained and tested on the conventional RGB image dataset and the SAVE-enhanced dataset. Additional repeated experimental assessments and statistical comparisons were also carried out to evaluate the improvement in performance. Results: The experimental results showed that the SAVE-based pre-processing always yielded better performance in terms of lesion detection than conventional RGB image processing. The SAVE framework for SSD was evaluated and compared with all other evaluated models and was found to be the most successful, with an accuracy of 96%, a precision of 97%, a recall of 96%, and an F1 score of 96%. Conclusions: The results indicate that the proposed SAVE framework could be a promising RGB-compatible spectral enhancement technique for boosting skin cancer detection and computer-aided dermatologic analysis with the aid of AI.<\/jats:p>","DOI":"10.3390\/diagnostics16121864","type":"journal-article","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T09:06:52Z","timestamp":1781600812000},"page":"1864","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["SAVE: Spectrum-Aided Visual Enhancement for AI-Based Skin Cancer Detection"],"prefix":"10.3390","volume":"16","author":[{"given":"Hung-Yi","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Dermatology, Ditmanson Medical Foundation Chiayi Christian Hospital, Chiayi 60002, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0530-3995","authenticated-orcid":false,"given":"Yaswanth","family":"Nagisetti","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chiayi 62102, Taiwan"}],"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 Biomedical Imaging, Chennai Institute of Technology, Sarathy Nagar, Chennai 600069, Tamil Nadu, India"},{"name":"Department of Computer Science Engineering, School of Engineering and Technology, Sanjivani University, Kopargaon 423603, Maharashtra, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0762-6770","authenticated-orcid":false,"given":"Riya","family":"Karmarkar","sequence":"additional","affiliation":[{"name":"Department of Integrated Bachelor of Technology, School of Engineering and Technology, Sanjivani University, Kopargaon 423603, Maharashtra, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sahaya Ashik","family":"Libu","sequence":"additional","affiliation":[{"name":"Department of Computer Science Engineering, Jeppiaar Engineering College, SH 49A, Chennai 600119, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao-Yuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, Kaohsiung Armed Forces General Hospital, 2, Zhongzheng 1st Rd., Lingya District, Kaohsiung City 80284, Taiwan"},{"name":"Department of Medicine, National Defense Medical University, No. 161, Sec. 6, Minquan E. Rd., Neihu District, Taipei City 11490, 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, 168, University Rd., Min Hsiung, Chiayi 62102, Taiwan"},{"name":"Department of Medical Research, Dalin Tzu Chi General Hospital, No. 2, Min-Sheng Rd., Dalin Town, Chiayi 62247, Taiwan"},{"name":"Hitspectra Intelligent Technology Co., Ltd., Kaohsiung City 80661, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,16]]},"reference":[{"key":"ref_1","first-page":"e70043","article-title":"Cancer statistics, 2026","volume":"76","author":"Siegel","year":"2026","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"103727","DOI":"10.1016\/j.micpro.2020.103727","article-title":"Diagnosis of skin cancer using machine learning techniques","volume":"81","author":"Murugan","year":"2021","journal-title":"Microprocess. Microsyst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1007\/s12325-019-01130-1","article-title":"Recognising skin cancer in primary care","volume":"37","author":"Jones","year":"2020","journal-title":"Adv. Ther."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5479","DOI":"10.3390\/ijerph18105479","article-title":"Skin cancer detection: A review using deep learning techniques","volume":"18","author":"Dildar","year":"2021","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1186\/s12943-023-01854-3","article-title":"Skin cancer: Understanding the journey of transformation from conventional to advanced treatment approaches","volume":"22","author":"Hasan","year":"2023","journal-title":"Mol. Cancer"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"m692","DOI":"10.1136\/bmj.m692","article-title":"When to suspect a non-melanoma skin cancer","volume":"368","author":"Smith","year":"2020","journal-title":"BMJ"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Adamus-Grabicka, A.A., Hikisz, P., and Sikora, J. (2024). Nanotechnology as a Promising Method in the Treatment of Skin Cancer. Int. J. Mol. Sci., 25.","DOI":"10.3390\/ijms25042165"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1007\/s40257-020-00517-z","article-title":"A review of noninvasive techniques for skin cancer detection in dermatology","volume":"21","author":"Heibel","year":"2020","journal-title":"Am. J. Clin. Dermatol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Fania, L., Didona, D., Morese, R., Campana, I., Coco, V., Di Pietro, F.R., Ricci, F., Pallotta, S., Candi, E., and Abeni, D. (2020). Basal cell carcinoma: From pathophysiology to novel therapeutic approaches. Biomedicines, 8.","DOI":"10.3390\/biomedicines8110449"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1007\/s13555-023-00888-1","article-title":"Advances in photodynamic therapy for the treatment of actinic keratosis and nonmelanoma skin cancer: A narrative review","volume":"13","author":"Farberg","year":"2023","journal-title":"Dermatol. Ther."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"adv00128","DOI":"10.2340\/00015555-3486","article-title":"Actinic keratosis diagnosis and increased risk of developing skin cancer: A 10-year cohort study of 17,651 patients in Sweden","volume":"100","author":"Guorgis","year":"2020","journal-title":"Acta Derm.-Venereol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1159\/000517070","article-title":"Advances in the etiology, detection, and clinical management of seborrheic keratoses","volume":"238","author":"Sun","year":"2022","journal-title":"Dermatology"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2632010X241255874","DOI":"10.1177\/2632010X241255874","article-title":"Seborrheic Keratosis: Report of a Rare Presentation and Reminder of the Current Knowledge of the Problem","volume":"17","author":"Lahbacha","year":"2024","journal-title":"Clin. Pathol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Jansen, P., Otero Baguer, D., Duschner, N., Le\u2019Clerc Arrastia, J., Schmidt, M., Wiepjes, B., Schenddorf, D., Hadashchik, E., Maass, P., and Schaller, J. (2022). Evaluation of a deep learning approach to differentiate Bowen\u2019s disease and seborrheic keratosis. Cancers, 14.","DOI":"10.3390\/cancers14143518"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.clindermatol.2023.12.022","article-title":"Artificial Intelligence in the detection of skin cancer: State of the art","volume":"42","author":"Strzelecki","year":"2024","journal-title":"Clin. Dermatol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1016\/j.det.2025.05.004","article-title":"Artificial Intelligence and Deep Learning for Skin Image Analysis","volume":"43","author":"Ohaya","year":"2025","journal-title":"Dermatol. Clin."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"TLin, T.-L., Mukundan, A., Karmakar, R., Avala, P., Chang, W.-Y., and Wang, H.-C. (2025). Hyperspectral Imaging for Enhanced Skin Cancer Classification Using Machine Learning. Bioengineering, 12.","DOI":"10.3390\/bioengineering12070755"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.1007\/s13246-024-01435-8","article-title":"Hyperspectral imaging with machine learning for in vivo skin carcinoma margin assessment: A preliminary study","volume":"47","author":"Parasca","year":"2024","journal-title":"Phys. Eng. Sci. Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"109570","DOI":"10.1016\/j.compbiomed.2024.109570","article-title":"Towards an integrated imaging for melanoma diagnosis: A review of multispectral, hyperspectral, and thermal technologies with preliminary system development","volume":"185","author":"Oniga","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Gouda, W., Sama, N.U., Al-Waakid, G., Humayun, M., and Jhanjhi, N.Z. (2022). Detection of skin cancer based on skin lesion images using deep learning. Healthcare, 10.","DOI":"10.3390\/healthcare10071183"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Agrahari, P., Agrawal, A., and Subhashini, N. (2022). Skin cancer detection using deep learning. Futuristic Communication and Network Technologies: Select Proceedings of VICFCNT 2020, Springer.","DOI":"10.1007\/978-981-16-4625-6_18"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"He, G.-Y., Su, C.-P., Chen, C.-S., Hsiang, Y.-S., Hu, W.-H., and Lee, S.-J. (2023). Actinic Keratosis Prediction Based on Deep Learning Methods. International Conference on Computer Recognition Systems, Springer.","DOI":"10.1007\/978-3-031-41630-9_12"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ahalya, R., Babu, G., Sathish, S., and Shruthi, K. (2024). Automated Skin Disease Detection Using Deep Learning Algorithms. 2024 10th International Conference on Communication and Signal Processing (ICCSP), IEEE.","DOI":"10.1109\/ICCSP60870.2024.10543405"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Mahum, R., and Aladhadh, S. (2022). Skin lesion detection using hand-crafted and DL-based features fusion and LSTM. Diagnostics, 12.","DOI":"10.3390\/diagnostics12122974"},{"key":"ref_25","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_26","doi-asserted-by":"crossref","unstructured":"Filho, A.R.G., Wastowski, I.J., Moreira, M.A.R., Cysneiros, M.A.d.P.C., and Coelho, C.J. (2023). Pancreatic Cancer Detection Using Hyperspectral Imaging and Machine Learning. 2023 IEEE International Conference on Image Processing (ICIP), IEEE.","DOI":"10.1109\/ICIP49359.2023.10222772"},{"key":"ref_27","first-page":"1128","article-title":"Applications of multispectral and hyperspectral imaging in dermatology","volume":"31","author":"Aggarwal","year":"2022","journal-title":"Exp. Dermatol."},{"key":"ref_28","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_29","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_30","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.1016\/j.crfs.2022.05.016","article-title":"Hyperspectral Imaging (HSI) for meat quality evaluation across the supply chain: Current and future trends","volume":"5","author":"Jia","year":"2022","journal-title":"Curr. Res. Food Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"151030","DOI":"10.1016\/j.scitotenv.2021.151030","article-title":"Application of hyperspectral imaging technology in the rapid identification of microplastics in farmland soil","volume":"807","author":"Ai","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, H., Feng, S., Wu, D., Zhao, C., Liu, X., Zhou, Y., Wang, S., Deng, H., and Zheng, S. (2024). Hyperspectral Image Classification on Large-Scale Agricultural Crops: The Heilongjiang Benchmark Dataset, Validation Procedure, and Baseline Results. Remote Sens., 16.","DOI":"10.3390\/rs16030478"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wu, I.-C., Chen, Y.-C., Karmakar, R., Mukundan, A., Gabriel, G., Wang, C.-C., and Wang, H.-C. (2024). Advancements in Hyperspectral Imaging and Computer-Aided Diagnostic Methods for the Enhanced Detection and Diagnosis of Head and Neck Cancer. Biomedicines, 12.","DOI":"10.3390\/biomedicines12102315"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1927","DOI":"10.1109\/JSTARS.2023.3242310","article-title":"Hyperspectral image band selection based on CNN embedded GA (CNNeGA)","volume":"16","author":"Esmaeili","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1109\/MGRS.2019.2911100","article-title":"Hyperspectral band selection: A review","volume":"7","author":"Sun","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1364\/OE.414050","article-title":"Improving characteristic band selection in leaf biochemical property estimation considering interrelations among biochemical parameters based on the PROSPECT-D model","volume":"29","author":"Yang","year":"2020","journal-title":"Opt. Express"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, N., Shi, Y., Li, H., Zhang, G., Li, S., and Liu, X. (2023). Multi-Prior Graph Autoencoder with Ranking-Based Band Selection for Hyperspectral Anomaly Detection. Remote Sens., 15.","DOI":"10.3390\/rs15184430"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"9147","DOI":"10.1109\/JSTARS.2021.3109455","article-title":"Constrained-target band selection with subspace partition for hyperspectral target detection","volume":"14","author":"Sun","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5","DOI":"10.33640\/2405-609X.3339","article-title":"YOLOv8-CAB: Improved YOLOv8 for Real-time object detection","volume":"10","author":"Talib","year":"2024","journal-title":"Karbala Int. J. Mod. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Sohan, M., Sai Ram, T., and Rami Reddy, C.V. (2024). A review on yolov8 and its advancements. International Conference on Data Intelligence and Cognitive Informatics, Springer.","DOI":"10.1007\/978-981-99-7962-2_39"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"42816","DOI":"10.1109\/ACCESS.2024.3378568","article-title":"Yolov1 to v8: Unveiling each variant\u2013a comprehensive review of yolo","volume":"12","author":"Hussain","year":"2024","journal-title":"IEEE Access"},{"key":"ref_42","unstructured":"Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., and Ding, G. (2024). Yolov10: Real-time end-to-end object detection. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"351","DOI":"10.3390\/solar4030016","article-title":"In-depth review of yolov1 to yolov10 variants for enhanced photovoltaic defect detection","volume":"4","author":"Hussain","year":"2024","journal-title":"Solar"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Akhmedov, F., Nasimov, R., and Abdusalomov, A. (2024). Dehazing Algorithm Integration with YOLO-v10 for Ship Fire Detection. Fire, 7.","DOI":"10.3390\/fire7090332"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Qiu, X., Chen, Y., Cai, W., Niu, M., and Li, J. (2024). LD-YOLOv10: A Lightweight Target Detection Algorithm for Drone Scenarios Based on YOLOv10. Electronics, 13.","DOI":"10.3390\/electronics13163269"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1364","DOI":"10.3390\/vehicles6030065","article-title":"Comparative Analysis of YOLOv8 and YOLOv10 in Vehicle Detection: Performance Metrics and Model Efficacy","volume":"6","author":"Geetha","year":"2024","journal-title":"Vehicles"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"An, Y., Tang, J., and Li, Y. (2022). A mobilenet SSDLite model with improved FPN for forest fire detection. Chinese Conference on Image and Graphics Technologies, Springer.","DOI":"10.1007\/978-981-19-5096-4_20"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Kang, H.-J. (2023). SSDLiteX: Enhancing SSDLite for Small Object Detection. Appl. Sci., 13.","DOI":"10.3390\/app132112001"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Anggraini, N., Ramadhani, S.H., Wardhani, L.K., Hakiem, N., Shofi, I.M., and Rosyadi, M.T. (2022). Development of Face Mask Detection using SSDLite MobilenetV3 Small on Raspberry Pi 4. 2022 5th International Conference of Computer and Informatics Engineering (IC2IE), IEEE.","DOI":"10.1109\/IC2IE56416.2022.9970078"},{"key":"ref_50","first-page":"1","article-title":"A new lightweight network based on MobileNetV3","volume":"16","author":"Zhao","year":"2022","journal-title":"KSII Trans. Internet Inf. Syst."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Qian, S., Ning, C., and Hu, Y. (2021). MobileNetV3 for image classification. 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), IEEE.","DOI":"10.1109\/ICBAIE52039.2021.9389905"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Muslim, H.A. (2024). Implementation of Chicken Eggs Fertility Detection Device Using SSD MobileNet-V2 FPNLite. 2024 International Electronics Symposium (IES), IEEE.","DOI":"10.1109\/IES63037.2024.10665784"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Gopal, R., Kuinthodu, S., Balamurugan, M., and Atique, M. (2019). Tiny object detection: Comparative study using single stage CNN object detectors. 2019 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE), IEEE.","DOI":"10.1109\/WIECON-ECE48653.2019.9019951"},{"key":"ref_54","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016). SSD: Single shot multibox detector. Proceedings of the Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, 11\u201314 October 2016, Springer. Proceedings, Part I 14."},{"key":"ref_55","unstructured":"Jocher, G., and Qiu, J. (2026, June 11). Ultralytics YOLO11. 2024. Ultralytics. YOLO11 Documentation. Available online: https:\/\/docs.ultralytics.com\/models\/yolo11\/."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/12\/1864\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T10:02:32Z","timestamp":1781604152000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/12\/1864"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,16]]},"references-count":55,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["diagnostics16121864"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16121864","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,16]]}}}