Ros‐NET: A deep convolutional neural network for automatic identification of rosacea lesions
Abstract
Background Rosacea is one of the most common cutaneous disorder characterized primarily by facial flushing, erythema, papules, pustules, telangiectases, and nasal swelling. Diagnosis of rosacea is principally done by a physical examination and a consistent patient history. However, qualitative human assessment is often subjective and suffers from a relatively high intra- and inter-observer variability in evaluating patient outcomes. Materials and methods To overcome these problems, we propose a quantitative and reproducible computer-aided diagnosis system, Ros-NET, which integrates information from different image scales and resolutions in order to identify rosacea lesions. This involves adaption of Inception-ResNet-v2 and ResNet-101 to extract rosacea features from facial images. Additionally, we propose to refine the detection results by means of facial-landmarks-based zones (ie, anthropometric landmarks) as regions of interest (ROI), which focus on typical areas of rosacea occurrence on a face. Results Using a leave-one-patient-out cross-validation scheme, the weighted average Dice coefficients, in percentages, across all patients (N = 41) with 256 × 256 image patches are 89.8 ± 2.6% and 87.8 ± 2.4% with Inception-ResNet-v2 and ResNet-101, respectively. Conclusion The findings from this study support that pre-trained networks trained via transfer learning can be beneficial in identifying rosacea lesions. Our future work will involve expanding the work to a larger database…
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Image-based deep learning models such as RosNet and FACES have been developed to classify rosacea.
“A few studies developed image-based deep learning models to classify rosacea, such as RosNet (adapted from Inception-ResNet-v2) and FACES (an ensemble deep learning model) (Binol et al, 2020; Park et al, 2023).”
From Artificial intelligence–enabled precision medicine for inflammatory skin diseases (2026) · cites this paper as Binol et al, 2020
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387 · see citing works
Statistical Methods for Rates and Proportions
Fleiss, J. L., Levin, B., & Paik, M. C. (2003) · Wiley Series in Probability and Statistics · doi:10.1002/0471445428 · Cited by 18,646 · see citing works
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332 · see citing works
SURF: Speeded Up Robust Features
Bay, H., Tuytelaars, T., & Van Gool, L. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_32 · Cited by 14,955 · see citing works
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · see citing works
How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014) · arXiv · doi:10.48550/arxiv.1411.1792 · Cited by 6,061 · see citing works
Deep learning in medical image analysis
Shen, D., Wu, G., & Suk, H.-I. (2017) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071516-044442 · Cited by 4,968 · see citing works
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636 · see citing works
The global k-means clustering algorithm
Likas, A. C., Vlassis, N. A., & Verbeek, J. J. (2002) · Pattern Recognition · doi:10.1016/s0031-3203(02)00060-2 · Cited by 3,755 · see citing works
Greenspan, H. K., van Ginneken, B., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2553401 · Cited by 1,704 · see citing works
Evolution of the neocortex: a perspective from developmental biology
Rakic, P. (2009) · Nature reviews. Neuroscience · doi:10.1038/nrn2719 · Cited by 1,565 · see citing works
Digital pathology and artificial intelligence
Niazi, M. K. K., Parwani, A. V., & Gurcan, M. N. (2019) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30154-8 · Cited by 1,232 · see citing works
Self-citationGallo, R. L., Granstein, R. D., Kang, S., Mannis, M. J., Steinhoff, M. S., Tan, J. K. L., & Thiboutot, D. M. (2017) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2017.08.037 · Cited by 513 · see citing works
Two, A. M., Wu, W., Gallo, R. L., & Hata, T. R. (2015) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2014.08.028 · Cited by 380 · see citing works
Morphometric Tools for Landmark Data: Geometry and Biology
Lele, S. R., & Bookstein, F. L. (1999) · Journal of the American Statistical Association · doi:10.2307/2669711 · Cited by 377 · see citing works
Rosacea. Pathophysiology and treatment
Wilkin, J. K. (1994) · Archives of Dermatology · doi:10.1001/archderm.130.3.359 · Cited by 209 · see citing works
Wilkin, J. K. (1994) · Archives of Dermatology · doi:10.1001/archderm.1994.01690030091015 · Cited by 164 · see citing works
On the depth of deep neural networks: a theoretical view
Sun, S., Chen, W., Wang, L., Liu, X., & Liu, T.-Y. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10243 · Cited by 147 · see citing works
Deep Learning for Medical Image Analysis
Şenaras, Ç., & Gürcan, M. N. (2018) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_27_18 · Cited by 140 · see citing works
Morphometric tools for landmark data; Geometry and biology
(1993) · General Pharmacology The Vascular System · doi:10.1016/0306-3623(93)90057-5 · Cited by 114 · see citing works
Statistical Methods for Rates and Proportions
Plackett, R. L., & Fleiss, J. L. (1975) · International Statistical Review · doi:10.2307/1402915 · Cited by 92 · see citing works
A comparison of CNN-based face and head detectors for real-time video surveillance applications
Nguyen-Meidine, L. T., Granger, É., Kiran, M., & Blais-Morin, L.-A. (2017) · International Conference on Image Processing Theory, Tools and Applications (IPTA) · doi:10.1109/ipta.2017.8310113 · Cited by 57 · see citing works
Identifying tumor in pancreatic neuroendocrine neoplasms from Ki67 images using transfer learning
Niazi, M. K. K., Tavolara, T. E., Arole, V., Hartman, D. J., Pantanowitz, L., & Gürcan, M. N. (2018) · PLoS ONE · doi:10.1371/journal.pone.0195621 · Cited by 51 · see citing works
Self-citationŞenaras, Ç., Niazi, M. K. K., Sahiner, B., Pennell, M., Tozbikian, G. H., Lozanski, G., & Gürcan, M. N. (2018) · PLoS ONE · doi:10.1371/journal.pone.0196846 · Cited by 47 · see citing works
Self-citationPattern matching using morphological probing
Barat, C., Ducottet, C., & Jourlin, M. (2004) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2003.1246975 · Cited by 30 · see citing works
Acne image analysis: lesion localization and classification
Abas, F. S., Kaffenberger, B. H., Bikowski, J., & Gürcan, M. N. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2216444 · Cited by 29 · see citing works
Self-citationSegmentation of Acne Vulgaris Lesions
Ramli, R., Malik, A. S., Hani, A. F. M., & Yap, F. B. (2011) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2011.63 · Cited by 17 · see citing works
Perceptual color hit-or-miss transform: application to dermatological image processing
Ledoux, A., Richard, N., Capelle-Laizé, A.-S., & Fernández-Maloigne, C. (2013) · Signal Image and Video Processing · doi:10.1007/s11760-013-0537-z · Cited by 12 · see citing works
Niazi, M. K. K., Beamer, G., & Gürcan, M. N. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2255627 · Cited by 11 · see citing works
Self-citationAutomated Detection of Facial Disorders (ADFD): a novel approach based-on digital photographs
Göçeri, E., & Günay, M. (2017) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2017.1317292 · Cited by 10 · see citing works
Binol, H., Niazi, M. K. K., Plotner, A. N., Sopkovich, J. A., Kaffenberger, B. H., & Gürcan, M. N. (2020) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.2549392 · Cited by 10 · see citing works
Self-citationGeneralization of tumor identification algorithms
Niazi, M. K. K., Tavolara, T. E., Şenaras, Ç., Tozbikian, G. H., Hartman, D. J., Arole, V., Pantanowitz, L., & Gürcan, M. N. (2019) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.2512911 · Cited by 4 · see citing works
Self-citation
Flagged first, then by citations
2 of the 37 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
A scoping review of transfer learning research on medical image analysis using ImageNet
Morid, M. A., Borjali, A., & Del Fiol, G. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104115 · Cited by 508 · see citing works
Daneshjou, R., Smith, M. P., Sun, M. D., Rotemberg, V., & Zou, J. (2021) · JAMA Dermatology · doi:10.1001/jamadermatol.2021.3129 · Cited by 403 · see citing works
Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations
Chan, S. Y., Reddy, V. D., Myers, B. A., Thibodeaux, Q. G., Brownstone, N. D., & Liao, W. J. (2020) · Dermatology and Therapy · doi:10.1007/s13555-020-00372-0 · Cited by 275 · see citing works
Deep learning in dermatology: a systematic review of current approaches, outcomes, and limitations
Jeong, H. K., Park, C., Henao, R., & Kheterpal, M. (2023) · JID Innovations · doi:10.1016/j.xjidi.2022.100150 · Cited by 134 · see citing works
Çamalan, S., Mahmood, H., Binol, H., Araújo, A. L. D., Santos‐Silva, A. R., Vargas, P. A., Lopes, M. A., Khurram, S. A., & Gürcan, M. N. (2021) · Cancers · doi:10.3390/cancers13061291 · Cited by 127 · see citing works
Self-citationAkram, A., Rashid, J., Jaffar, M. A., Faheem, M., & Amin, R. U. (2023) · Skin Research and Technology · doi:10.1111/srt.13524 · Cited by 82 · see citing works
Change Detection of Amazonian Alluvial Gold Mining Using Deep Learning and Sentinel-2 Imagery
Çamalan, S., Cui, K., Pauca, V. P., Alqahtani, S., Silman, M. R., Chan, R. H., Plemmons, R. J., Dethier, E. N., Fernandez, L. E., & Lutz, D. A. (2022) · Remote Sensing · doi:10.3390/rs14071746 · Cited by 66 · see citing works
Zhao, Z., Wu, C.-M., Zhang, S., He, F., Liu, F., Wang, B., Huang, Y., Shi, W., Jian, D., Xie, H., et al. (2021) · JMIR Medical Informatics · doi:10.2196/23415 · Cited by 58 · see citing works
“However, previous work focused mainly on the development of networks or analysis of images instead of practically applying CNN for the identification of rosacea and differentiating it from other skin diseases or for the classification of subtypes of rosacea [32].”
BackgroundArtificial intelligence in dermatology and healthcare: An overview
Pai, V. V., & Pai, R. B. (2021) · Indian Journal of Dermatology Venereology and Leprology · doi:10.25259/ijdvl_518_19 · Cited by 53 · see citing works
OtoMatch: Content-based eardrum image retrieval using deep learning
Çamalan, S., Niazi, M. K. K., Moberly, A. C., Teknos, T. N., Essig, G., Elmaraghy, C., Taj‐Schaal, N., & Gürcan, M. N. (2020) · PLoS ONE · doi:10.1371/journal.pone.0232776 · Cited by 53 · see citing works
Self-citationAI-assisted facial analysis in healthcare: From disease detection to comprehensive management
Lei, C., Dang, K., Song, S., Wang, Z., Chew, S. P., Bian, R., Yang, X., Guan, Z., Lopes, C. I. M. D. A., Wang, M. H., et al. (2025) · Patterns · doi:10.1016/j.patter.2025.101175 · Cited by 33 · see citing works
Pomi, F. L., Papa, V., Borgia, F., Vaccaro, M., Pioggia, G., & Gangemi, S. (2024) · Life · doi:10.3390/life14040516 · Cited by 33 · see citing works
A Multi-model Deep Learning Architecture for Diagnosing Multi-class Skin Diseases
Badr, M., Elkasaby, A., Alrahmawy, M. F., & El‐Metwally, S. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01300-w · Cited by 34 · see citing works
Skin Disease Analysis With Limited Data in Particular Rosacea: A Review and Recommended Framework
Mohanty, A., Sutherland, A., Bezbradica, M., & Javidnia, H. (2022) · IEEE Access · doi:10.1109/access.2022.3165574 · Cited by 31 · see citing works
Effectiveness of VISIA system in evaluating the severity of rosacea
Pan, Y., Jia, K., Yan, S., & Jiang, X. L. (2022) · Skin Research and Technology · doi:10.1111/srt.13194 · Cited by 28 · see citing works
Differential Diagnosis of Rosacea Using Machine Learning and Dermoscopy
Ge, L., Li, Y., Wu, Y., Fan, Z., & Song, Z. (2022) · Clinical Cosmetic and Investigational Dermatology · doi:10.2147/ccid.s373534 · Cited by 23 · see citing works
Binol, H., Moberly, A. C., Niazi, M. K. K., Essig, G., Shah, J., Elmaraghy, C., Teknos, T. N., Taj‐Schaal, N., Yu, L., & Gürcan, M. N. (2020) · Applied Sciences · doi:10.3390/app10175894 · Cited by 23 · see citing works
Self-citationRETRACTED: Lyme rashes disease classification using deep feature fusion technique
Ali, G., Anwar, M., Nauman, M., Faheem, M., & Rashid, J. (2023) · Skin Research and Technology · doi:10.1111/srt.13519 · Cited by 20 · see citing works
RetractedWillem, T., Krammer, S., Böhm, A. ‐., French, L. E., Hartmann, D., Lasser, T., & Buyx, A. (2022) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/jdv.18192 · Cited by 18 · see citing works
Binol, H., Niazi, M. K. K., Elmaraghy, C., Moberly, A. C., & Gürcan, M. N. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07107-6 · Cited by 18 · see citing works
Self-citationInteligencia artificial en dermatología: ¿amenaza u oportunidad?
Martorell, A., Martín-Gorgojo, A., Ríos‐Viñuela, E., Rueda-Carnero, J. M., Alfageme, F., & Taberner, R. (2021) · Actas Dermo-Sifiliográficas · doi:10.1016/j.ad.2021.07.003 · Cited by 18 · see citing works
Improved CNN architecture for automated classification of skin diseases
Deepak, G. D., Bhat, S. K., & Gupta, A. (2024) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2024.2420727 · Cited by 14 · see citing works
FACES: A Deep-Learning-Based Parametric Model to Improve Rosacea Diagnoses
Park, S., Chien, A. L., Lin, B., & Li, K. (2023) · Applied Sciences · doi:10.3390/app13020970 · Cited by 12 · see citing works
Huang, Y., He, J.-Y., Zhang, S., Tang, Y., Wang, B., Jian, D., Xie, H., Li, J., Chen, F., & Zhao, Z. (2022) · PeerJ · doi:10.7717/peerj.13917 · Cited by 11 · see citing works
Zhang, Y., Hu, Y., Li, K., Pan, X., Mo, X., & Zhang, H. (2024) · Digital Health · doi:10.1177/20552076241257087 · Cited by 9 · see citing works
TTDCapsNet: Tri Texton-Dense Capsule Network for complex and medical image recognition
Akoto-Adjepong, V., Appiah, O., Mensah, P. K., & Appiahene, P. (2024) · PLoS ONE · doi:10.1371/journal.pone.0300133 · Cited by 9 · see citing works
Modern AI/ML Methods for Healthcare: Opportunities and Challenges
Garg, A., Venkataramani, V. V., Karthikeyan, A., & Priyakumar, U. D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-94876-4_1 · Cited by 8 · see citing works
[Translated article] Artificial intelligence in dermatology: A threat or an opportunity?
Martorell, A., Martín-Gorgojo, A., Ríos‐Viñuela, E., Rueda-Carnero, J. M., Alfageme, F., & Taberner, R. (2022) · Actas Dermo-Sifiliográficas · doi:10.1016/j.ad.2021.07.014 · Cited by 9 · see citing works
The emerging role of Artificial Intelligence in diagnosis and clinical analysis of dermatology
Ye, S., & Chen, M. (2023) · Dermatologica Sinica · doi:10.4103/ds.ds-d-23-00025 · Cited by 7 · see citing works
Artificial Intelligence in Translational Medicine
Brogi, S., & Calderone, V. (2021) · International Journal of Translational Medicine · doi:10.3390/ijtm1030016 · Cited by 8 · see citing works
Artificial intelligence–enabled precision medicine for inflammatory skin diseases
Tang, A. S., Wei, M. L., Haemel, A., La, C., Sirota, M., & Lee, E. Y. (2026) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2025.10.596 · Cited by 6 · see citing works
Mao, R., & Li, J. (2024) · BMC Medical Genomics · doi:10.1186/s12920-024-02008-0 · Cited by 5 · see citing works
Hodson, E. L., Salem, I., Birkner, M., Sriharan, A., Dagrosa, A. T., Davis, M., & Hamann, C. R. (2023) · JAAD Case Reports · doi:10.1016/j.jdcr.2023.05.031 · Cited by 5 · see citing works
Skinformatics: Navigating the big data landscape of dermatology
Guermazi, D., Shah, A., Yumeen, S., Vance, T. M., & Saliba, E. (2024) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/jdv.20319 · Cited by 5 · see citing works
Towards Synthetic Generation of Clinical Rosacea Images with GAN Models
Mohanty, A., Sutherland, A., Bezbradica, M., & Javidnia, H. (2022) · Irish Signals and Systems Conference · doi:10.1109/issc55427.2022.9826207 · Cited by 4 · see citing works
Retracted: Acne and Rosacea Detection from Images using Deep CNN's EfficientNet
Prodeep, A. R., Araf, R., Ray, P., Ulubbi, M. S. A., Ananna, S. N., & Mridha, M. F. (2022) · International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) · doi:10.1109/accai53970.2022.9752534 · Cited by 4 · see citing works
RetractedChen, M., Wang, X., Han, M., Li, Y., Yu, N., Long, X., & Chen, W. (2023) · Frontiers in Nutrition · doi:10.3389/fnut.2023.1115079 · Cited by 4 · see citing works
High-Fidelity Synthetic Face Generation for Rosacea Skin Condition from Limited Data
Mohanty, A., Sutherland, A., Bezbradica, M., & Javidnia, H. (2024) · Electronics · doi:10.3390/electronics13020395 · Cited by 5 · see citing works
Advancements in acne detection and classification: A bibliometric analysis
Paluri, K. V., Nain, G., & Gupta, A. (2025) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2025.110693 · Cited by 3 · see citing works
Artificial intelligence-assisted prediction of Demodex mite density in facial erythema
Kim, J., Lee, Y. N., Boo, J., Oh, I., Lee, C., Lee, J. H., Choi, Y. S., Kim, H. J., Na, J., Kim, J., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-29791-9 · Cited by 2 · see citing works
Sapaico-Alberto, A. F., Olaya-Cotera, S., & Flores-Castañeda, R. O. (2024) · Archives of Dermatological Research · doi:10.1007/s00403-024-03650-5 · Cited by 2 · see citing works
Artificial intelligence-enabled precision medicine for inflammatory skin diseasesPreprint
Tang, A., Wei, M. L., Haemel, A. K., La, C., Sirota, M., & Lee, E. Y. (2025) · PubMed · doi:10.48550/arxiv.2505.09527 · Cited by 5 · see citing works
Duan, L., Li, T., Li, B., Li, X., Fu, D., Yang, X., Cao, K., & Cai, H. (2025) · Intelligent Medicine · doi:10.1016/j.imed.2025.08.004 · Cited by 2 · see citing works
Yang, C., Yesgari, R. R., & Liu, C. (2025) · International Conference on Electrical and Computer Engineering Researches (ICECER) · doi:10.1109/icecer65523.2025.11401323 · Cited by 1 · see citing works
Deep Learning-Based Multi-class Skin Disease Diagnosis Toward Sustainable Healthcare
Nassar, Y. M., Makhlouf, M., El-tohamy, M. H., Abdelgawad, W., Darwish, E., Abdalla, H., & Sayed, G. I. (2025) · Springer eBooks · doi:10.1007/978-3-031-78038-7_7 · Cited by 1 · see citing works
High Fidelity Synthetic Face Generation for Rosacea Skin Condition from Limited DataPreprint
Mohanty, A., Sutherland, A., Bezbradica, M., & Javidnia, H. (2023) · Research Square · doi:10.21203/rs.3.rs-2987104/v1 · Cited by 1 · see citing works
Artificial Intelligence in Dermatology: A Case Study for Facial Skin Diseases
Saleh, R. E., Zein, H., Chantaf, S., & Naït-Ali, A. (2021) · Computational biology · doi:10.1007/978-3-030-69951-2_7
WITHDRAWN: Artificial intelligence in dermatology: A threat or an opportunity?
Martorell, A., Martín-Gorgojo, A., Ríos‐Viñuela, E., Rueda-Carnero, J. M., Alfageme, F., & Taberner, R. (2021) · Actas Dermo-Sifiliográficas · doi:10.1016/j.adengl.2021.11.007
Retracted35-45 METŲ MOTERŲ, VARTOJUSIŲ X MAISTO PAPILDĄ, VEIDO ERITEMOS IR BENDRO KRAUJO RODIKLIŲ SĄSAJOS
Kirvaitienė, J., Volskienė, R., & Ferrer, D. B. (2023) · Health Sciences · doi:10.35988/sm-hs.2023.276
Revolutionizing Rosacea Treatment with Novel Drug Delivery Systems: A Review
Patel, H., Jani, R., & Patel, N. (2024) · Research Journal of Pharmacy and Technology · doi:10.52711/0974-360x.2024.00540
Optimizing Lyme Bacterial Disease Identification with Fine-Tuned EfficientnetB0 Model
Shandilya, G., & Anand, V. (2024) · International Conference on Technological Advancements in Computational Sciences (ICTACS) · doi:10.1109/ictacs62700.2024.10840526
Mobile Application Using Convolutional Neural Networks for Preliminary Diagnosis of Rosacea
Sapaico-Alberto, A. F., & Flores-Castañeda, R. O. (2025) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2025.0160637
Wang, Y., Luo, H., Song, D., Duan, M., Han, Y., Wang, W., Feng, Y., & Jiang, X. L. (2026) · Dermatology · doi:10.1159/000550245
Facial Skin Disease Classification using EfficientNetV2B0 with Attention-Based BiGRU
Ilmi, N., Fajriyah, S. Z., Haryani, E., & Daratullah, I. M. (2025) · International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) · doi:10.1109/isriti68345.2025.11393174
AI-Powered Approach to Rosacea
Kyrmanidou, E., & Lazaridou, E. (2026) · Springer eBooks · doi:10.1007/978-3-032-15614-3_12
Surya, B. S., Shilpa, V., Karthik, P., Karthik, B. U., Venugopal, S., Sapna, Bharani, B. R., & Girija, V. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-02483-7
- Authors
- Binol, H., Plotner, A., Sopkovich, J., Kaffenberger, B., Niazi, M. K. K., & Gurcan, M. N.
- Affiliations
- Wake Forest University Health Sciences (US)
- The Ohio State University (US)
- Center for Biomedical Informatics Wake Forest School of Medicine Winston‐Salem NC USA
- Corresponding author
- Binol, Hamidullah
- Venue
- Skin Research and Technology
- Publisher
- Wiley
- Published
- 2019-12-17
- Volume
- 26
- Issue
- 3
- Pages
- 413-421
- ISSN
- 0909-752X (ISSN-L)
- 1600-0846
- OpenAlex
- W2994949090
- Cited by
- 56
- References
- 37
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 11 · 2023: 8 · 2022: 12 · 2021: 7 · 2020: 5
- FWCI
- 6.05
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Skin Research and Technology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Rosacea Society
- Topics
- Health Sciences › Medicine › Dermatology › Acne and Rosacea Treatments and Effects
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- rosacea, computer-aided diagnosis, deep convolutional neural networks, InceptionResNetV2, ResNet-101, transfer learning, face images, facial landmarks, region of interest
- MeSH terms
- Machine Learning, Deep Learning, Algorithms, Diagnosis, Computer-Assisted, Face, Female, Humans, Image Processing, Computer-Assisted, Male, Rosacea, Skin Diseases, Observer Variation, Neural Networks, Computer, Anatomic Landmarks
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
