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For: Debelee TG, Schwenker F, Ibenthal A, Yohannes D. Survey of deep learning in breast cancer image analysis. Evolving Systems 2020;11:143-63. [DOI: 10.1007/s12530-019-09297-2] [Cited by in Crossref: 20] [Cited by in F6Publishing: 4] [Article Influence: 6.7] [Reference Citation Analysis]
Number Citing Articles
1 El Jurdi R, Petitjean C, Honeine P, Cheplygina V, Abdallah F. High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding 2021;210:103248. [DOI: 10.1016/j.cviu.2021.103248] [Cited by in Crossref: 3] [Article Influence: 3.0] [Reference Citation Analysis]
2 Wang YY, Hamad AS, Palaniappan K, Lever TE, Bunyak F. LARNet-STC: Spatio-temporal orthogonal region selection network for laryngeal closure detection in endoscopy videos. Computers in Biology and Medicine 2022;144:105339. [DOI: 10.1016/j.compbiomed.2022.105339] [Reference Citation Analysis]
3 Waldamichael FG, Debelee TG, Schwenker F, Ayano YM, Kebede SR. Machine Learning in Cereal Crops Disease Detection: A Review. Algorithms 2022;15:75. [DOI: 10.3390/a15030075] [Reference Citation Analysis]
4 Yuan Z, Puyol-antón E, Jogeesvaran H, Smith N, Inusa B, King AP. Deep learning-based quality-controlled spleen assessment from ultrasound images. Biomedical Signal Processing and Control 2022;76:103724. [DOI: 10.1016/j.bspc.2022.103724] [Reference Citation Analysis]
5 Waldamichael FG, Debelee TG, Ayano YM. Coffee disease detection using a robust HSV color‐based segmentation and transfer learning for use on smartphones. Int J Intell Syst. [DOI: 10.1002/int.22747] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
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7 Gómez-Flores W, Coelho de Albuquerque Pereira W. A comparative study of pre-trained convolutional neural networks for semantic segmentation of breast tumors in ultrasound. Comput Biol Med 2020;126:104036. [PMID: 33059238 DOI: 10.1016/j.compbiomed.2020.104036] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
8 Abdelrahman L, Al Ghamdi M, Collado-Mesa F, Abdel-Mottaleb M. Convolutional neural networks for breast cancer detection in mammography: A survey. Comput Biol Med 2021;131:104248. [PMID: 33631497 DOI: 10.1016/j.compbiomed.2021.104248] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
9 Livieris IE, Stavroyiannis S, Pintelas E, Kotsilieris T, Pintelas P. A dropout weight-constrained recurrent neural network model for forecasting the price of major cryptocurrencies and CCi30 index. Evolving Systems 2022;13:85-100. [DOI: 10.1007/s12530-020-09361-2] [Cited by in Crossref: 3] [Article Influence: 3.0] [Reference Citation Analysis]
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12 Oza P, Sharma P, Patel S, Kumar P. Deep convolutional neural networks for computer-aided breast cancer diagnostic: a survey. Neural Comput & Applic 2022;34:1815-36. [DOI: 10.1007/s00521-021-06804-y] [Reference Citation Analysis]
13 Karthik R, Menaka R, Kathiresan G, Anirudh M, Nagharjun M. Gaussian Dropout Based Stacked Ensemble CNN for Classification of Breast Tumor in Ultrasound Images. IRBM 2021. [DOI: 10.1016/j.irbm.2021.10.002] [Reference Citation Analysis]
14 Chugh G, Kumar S, Singh N. Survey on Machine Learning and Deep Learning Applications in Breast Cancer Diagnosis. Cogn Comput 2021;13:1451-70. [DOI: 10.1007/s12559-020-09813-6] [Cited by in Crossref: 9] [Cited by in F6Publishing: 3] [Article Influence: 9.0] [Reference Citation Analysis]
15 Elameer AS, Jaber MM, Abd SK. Radiography image analysis using cat swarm optimized deep belief networks. Journal of Intelligent Systems 2021;31:40-54. [DOI: 10.1515/jisys-2021-0172] [Reference Citation Analysis]
16 Choudhary T, Mishra V, Goswami A, Sarangapani J. A transfer learning with structured filter pruning approach for improved breast cancer classification on point-of-care devices. Comput Biol Med 2021;134:104432. [PMID: 33964737 DOI: 10.1016/j.compbiomed.2021.104432] [Reference Citation Analysis]