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Cited by in F6Publishing
For: Naik A, Edla DR, Dharavath R. Prediction of Malignancy in Lung Nodules Using Combination of Deep, Fractal, and Gray-Level Co-Occurrence Matrix Features. Big Data 2021;9:480-98. [PMID: 34191590 DOI: 10.1089/big.2020.0190] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
Number Citing Articles
1 Segall RS, Sankarasubbu V. Survey of Recent Applications of Artificial Intelligence for Detection and Analysis of COVID-19 and Other Infectious Diseases. International Journal of Artificial Intelligence and Machine Learning 2022;12:1-30. [DOI: 10.4018/ijaiml.313574] [Reference Citation Analysis]
2 Saihood A, Karshenas H, Nilchi ARN. Deep fusion of gray level co-occurrence matrices for lung nodule classification. PLoS ONE 2022;17:e0274516. [DOI: 10.1371/journal.pone.0274516] [Reference Citation Analysis]
3 Wei X, Yan XJ, Guo YY, Zhang J, Wang GR, Fayyaz A, Yu J. Machine learning-based gray-level co-occurrence matrix signature for predicting lymph node metastasis in undifferentiated-type early gastric cancer. World J Gastroenterol 2022; 28(36): 5338-5350 [DOI: 10.3748/wjg.v28.i36.5338] [Reference Citation Analysis]
4 Han M, Du S, Ge Y, Zhang D, Chi Y, Long H, Yang J, Yang Y, Xin J, Chen T, Zheng N, Guo Y. With or without human interference for precise age estimation based on machine learning? Int J Legal Med. [DOI: 10.1007/s00414-022-02796-z] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
5 Zhao L, Bai G, Ji Y, Peng Y, Zang R, Gao S. Consolidation Tumor Ratio Combined With Pathological Features Could Predict Status of Lymph Nodes of Early-Stage Lung Adenocarcinoma. Front Oncol 2021;11:749643. [PMID: 35096566 DOI: 10.3389/fonc.2021.749643] [Reference Citation Analysis]