ResNet50-Based Effective Model for Breast Cancer Classification Using Histopathology Images
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2022/01/01
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Indian UGC (journal)
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Refrences43
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Citations4
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Nishant Behar
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Manish Shrivastava
Cite
Behar, Nishant, and Manish Shrivastava. “ResNet50-Based Effective Model for Breast Cancer Classification Using Histopathology Images”. Computer Modeling in Engineering &Amp; Sciences, vol. 130, no. 2, 2022, pp. 823-39, https://doi.org/10.32604/cmes.2022.017030.
Behar, N., & Shrivastava, M. (2022). ResNet50-Based Effective Model for Breast Cancer Classification Using Histopathology Images. Computer Modeling in Engineering &Amp; Sciences, 130(2), 823-839. https://doi.org/10.32604/cmes.2022.017030
Behar N, Shrivastava M. ResNet50-Based Effective Model for Breast Cancer Classification Using Histopathology Images. Computer Modeling in Engineering & Sciences. 2022;130(2):823-39.
Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
Reliability analysis of mooring lines for floating structures using ANN-BN inference | Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment |
| 3 | 2021 |
| Implementation of hydraulically driven barrel shooting control by utilizing artificial neural networks | Mathematics and Computers in Simulation |
| 2 | 2021 |
| Second-order Emden–Fowler neutral differential equations: A new precise criterion for oscillation | Applied Mathematics Letters |
| 25 | 2021 |
| Solving a novel designed second order nonlinear Lane–Emden delay differential model using the heuristic techniques | Applied Soft Computing |
| 63 | 2021 |
| A new transfer learning based approach to magnification dependent and independent classification of breast cancer in histopathological images | Biomedical Signal Processing and Control |
| 77 | 2021 |
Refrences Analysis
The category
Science: Mathematics: Instruments and machines: Electronic computers. Computer science 12
is the most frequently represented among the references in this article. It primarily includes studies from IEEE Access and Medical Image Analysis. The chart below illustrates the number of referenced publications per year.
Refrences used by this article by year
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Recognition of geological legends on a geological profile via an improved deep learning method with augmented data using transfer learning strategies | Ore Geology Reviews |
| 2 | 2023 |
| Zero Watermarking Algorithm for Medical Image Based on Resnet50-DCT | Computers, Materials & Continua | 2023 | ||
| A Survey of Convolutional Neural Network in Breast Cancer | Computer Modeling in Engineering & Sciences | 5 | 2023 | |
| State of the Art of Artificial Intelligence Applications in Oncology | Open Journal of Applied Sciences | 2023 |
Citations Analysis
| Category | Category Repetition |
|---|---|
| Science: Geology | 1 |
| Technology: Mining engineering. Metallurgy | 1 |
The category
Science: Geology 1
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled State of the Art of Artificial Intelligence Applications in Oncology and was published in 2023. The most recent citation comes from a 2023 study titled State of the Art of Artificial Intelligence Applications in Oncology. This article reached its peak citation in 2023, with 4 citations. It has been cited in 4 different journals, 25% of which are open access. Among related journals, the Open Journal of Applied Sciences cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.