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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Deep‐features with Bayesian optimized classifiers for the breast cancer diagnosis
S. R. Sannasi Chakravarthy, Harikumar Rajaguru
International Journal of Imaging Systems and Technology (2021) Vol. 31, Iss. 4, pp. 1861-1881
Closed Access | Times Cited: 13

Showing 13 citing articles:

Deep Learning-Based Metaheuristic Weighted K-Nearest Neighbor Algorithm for the Severity Classification of Breast Cancer
S. R. Sannasi Chakravarthy, N. Bharanidharan, Harikumar Rajaguru
IRBM (2023) Vol. 44, Iss. 3, pp. 100749-100749
Closed Access | Times Cited: 50
Multi-class Breast Cancer Classification Using CNN Features Hybridization
S. R. Sannasi Chakravarthy, N. Bharanidharan, Surbhi Bhatia, et al.
International Journal of Computational Intelligence Systems (2024) Vol. 17, Iss. 1
Open Access | Times Cited: 2
A Systematic Review on Screening, Examining and Classification of Breast Cancer
S. R. Sannasi Chakravarthy, Harikumar Rajaguru
2022 Smart Technologies, Communication and Robotics (STCR) (2021), pp. 1-4
Closed Access | Times Cited: 17
Deep learning-based breast cancer detection using VGG-NiN architecture
S. R. Sannasi Chakravarthy, Harikumar Rajaguru
AIP conference proceedings (2023) Vol. 2725, pp. 020001-020001
Open Access | Times Cited: 5
A systematic review on machine learning algorithms used for forecasting lake‐water level fluctuations
S. R. Sannasi Chakravarthy, N. Bharanidharan, Harikumar Rajaguru
Concurrency and Computation Practice and Experience (2022) Vol. 34, Iss. 24
Closed Access | Times Cited: 8
SKMAT‐U‐Net architecture for breast mass segmentation
S. R. Sannasi Chakravarthy, Harikumar Rajaguru
International Journal of Imaging Systems and Technology (2022) Vol. 32, Iss. 6, pp. 1880-1888
Closed Access | Times Cited: 5
A Comprehensive Study on Machine Learning in Breast Cancer Detection and Classification
Ali Bou Nassif, Aya Al-Chikh Omar
(2022), pp. 81-87
Closed Access | Times Cited: 5
Performance evaluation of machine learning techniques for breast cancer detection using WDBC dataset
Indu Chhillar, Ajmer Singh
AIP conference proceedings (2024)
Open Access
A Systematic Literature Review on Mammography: Deep Learning in Redefining Breast Cancer Diagnosis for the Asian Perspective
Ashwini Amin, U. Dinesh Acharya, P. C. Siddalingaswamy, et al.
Research Square (Research Square) (2024)
Open Access
Meteorological-Data-Driven Rubber Tree Powdery Mildew Model and Its Application on Spatiotemporal Patterns: A Case Study of Hainan Island
Jiayan Kong, Yinghe An, Xian Shi, et al.
Sustainability (2023) Vol. 15, Iss. 16, pp. 12119-12119
Open Access | Times Cited: 1
BCUIS‐Net: A breast cancer ultrasound image segmentation network via boundary‐aware and shape feature fusion
Haiyan Li, Xu Wang, Yiyin Tang, et al.
International Journal of Imaging Systems and Technology (2023) Vol. 34, Iss. 1
Closed Access | Times Cited: 1
Breast cancer: Three‐class masses classification in mammograms using Apriori dynamic selection
Khaoula Belhaj Soulami, Naima Kaabouch, M. Saïdi
Concurrency and Computation Practice and Experience (2022) Vol. 34, Iss. 24
Closed Access | Times Cited: 2
Fusion of Deep Features for Classification of Breast Cancer Using Multi-Deep CNNs
S. R. Sannasi Chakravarthy, N. Bharanidharan, Harikumar Rajaguru
IFMBE proceedings (2022), pp. 49-57
Closed Access
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