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Cited by in F6Publishing
For: Jubair F, Al-Karadsheh O, Malamos D, Al Mahdi S, Saad Y, Hassona Y. A novel lightweight deep convolutional neural network for early detection of oral cancer. Oral Dis 2021. [PMID: 33636041 DOI: 10.1111/odi.13825] [Cited by in Crossref: 17] [Cited by in F6Publishing: 17] [Article Influence: 17.0] [Reference Citation Analysis]
Number Citing Articles
1 Deif MA, Attar H, Amer A, Elhaty IA, Khosravi MR, Solyman AAA, Kumar A. Diagnosis of Oral Squamous Cell Carcinoma Using Deep Neural Networks and Binary Particle Swarm Optimization on Histopathological Images: An AIoMT Approach. Computational Intelligence and Neuroscience 2022;2022:1-13. [DOI: 10.1155/2022/6364102] [Reference Citation Analysis]
2 Bansal K, Bathla RK, Kumar Y. Deep transfer learning techniques with hybrid optimization in early prediction and diagnosis of different types of oral cancer. Soft Comput. [DOI: 10.1007/s00500-022-07246-x] [Reference Citation Analysis]
3 Ferro A, Kotecha S, Fan K. Machine learning in point-of-care automated classification of oral potentially malignant and malignant disorders: a systematic review and meta-analysis. Sci Rep 2022;12:13797. [PMID: 35963880 DOI: 10.1038/s41598-022-17489-1] [Reference Citation Analysis]
4 Hegde S, Ajila V, Zhu W, Zeng C. Review of the Use of Artificial Intelligence in Early Diagnosis and Prevention of Oral Cancer. Asia-Pacific Journal of Oncology Nursing 2022. [DOI: 10.1016/j.apjon.2022.100133] [Reference Citation Analysis]
5 Kim J, Kim BG, Hwang SH. Efficacy of Artificial Intelligence-Assisted Discrimination of Oral Cancerous Lesions from Normal Mucosa Based on the Oral Mucosal Image: A Systematic Review and Meta-Analysis. Cancers 2022;14:3499. [DOI: 10.3390/cancers14143499] [Reference Citation Analysis]
6 Alabi RO, Almangush A, Elmusrati M, Leivo I, Mäkitie A. Measuring the Usability and Quality of Explanations of a Machine Learning Web-Based Tool for Oral Tongue Cancer Prognostication. IJERPH 2022;19:8366. [DOI: 10.3390/ijerph19148366] [Reference Citation Analysis]
7 Li C, Zhang Q, Sun K, Jia H, Shen X, Tang G, Liu W, Shi L. Autofluorescence imaging as a noninvasive tool of risk stratification for malignant transformation of oral leukoplakia: A follow-up cohort study. Oral Oncology 2022;130:105941. [DOI: 10.1016/j.oraloncology.2022.105941] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Zaman U, Imran, Mehmood F, Iqbal N, Kim J, Ibrahim M. Towards Secure and Intelligent Internet of Health Things: A Survey of Enabling Technologies and Applications. Electronics 2022;11:1893. [DOI: 10.3390/electronics11121893] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 7.0] [Reference Citation Analysis]
9 Schnyder Jason D A, Krishnan V, Vinayachandran D. Intelligent systems for precision dental diagnosis and treatment planning – A review. Cumhuriyet Dental Journal 2022. [DOI: 10.7126/cumudj.991480] [Reference Citation Analysis]
10 Alabi RO, Bello IO, Youssef O, Elmusrati M, Mäkitie AA, Almangush A. Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma-A Systematic Review. Front Oral Health 2021;2:686863. [PMID: 35048032 DOI: 10.3389/froh.2021.686863] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 5.0] [Reference Citation Analysis]
11 Alabi RO, Almangush A, Elmusrati M, Mäkitie AA. Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine. Front Oral Health 2022;2:794248. [DOI: 10.3389/froh.2021.794248] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
12 Singh A, Sahu A, Verma S. Computer Intelligence in Detection of Malignant or Premalignant Oral Lesions: The Story So Far. Computational Intelligence in Oncology 2022. [DOI: 10.1007/978-981-16-9221-5_11] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
13 Xie F, Zhang P, Jiang T, She J, Shen X, Xu P, Zhao W, Gao G, Guan Z. Lesion Segmentation Framework Based on Convolutional Neural Networks with Dual Attention Mechanism. Electronics 2021;10:3103. [DOI: 10.3390/electronics10243103] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
14 Duran-Sierra E, Cheng S, Cuenca R, Ahmed B, Ji J, Yakovlev VV, Martinez M, Al-Khalil M, Al-Enazi H, Cheng YL, Wright J, Busso C, Jo JA. Machine-Learning Assisted Discrimination of Precancerous and Cancerous from Healthy Oral Tissue Based on Multispectral Autofluorescence Lifetime Imaging Endoscopy. Cancers (Basel) 2021;13:4751. [PMID: 34638237 DOI: 10.3390/cancers13194751] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 6.0] [Reference Citation Analysis]
15 Khanagar SB, Naik S, Al Kheraif AA, Vishwanathaiah S, Maganur PC, Alhazmi Y, Mushtaq S, Sarode SC, Sarode GS, Zanza A, Testarelli L, Patil S. Application and Performance of Artificial Intelligence Technology in Oral Cancer Diagnosis and Prediction of Prognosis: A Systematic Review. Diagnostics (Basel) 2021;11:1004. [PMID: 34072804 DOI: 10.3390/diagnostics11061004] [Cited by in Crossref: 14] [Cited by in F6Publishing: 16] [Article Influence: 14.0] [Reference Citation Analysis]
16 Welikala RA, Remagnino P, Lim JH, Chan C, Rajendran S, Kallarakkal TG, Zain RB, Jayasinghe RD, Rimal J, Kerr AR, Amtha R, Patil K, Tilakaratne W, Cheong SC, Barman SA. Clinically Guided Trainable Soft Attention for Early Detection of Oral Cancer. Computer Analysis of Images and Patterns 2021. [DOI: 10.1007/978-3-030-89128-2_22] [Reference Citation Analysis]