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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 Convolutional Neural Networks for breast cancer screening
Hiba Chougrad, Hamid Zouaki, Omar Alheyane
Computer Methods and Programs in Biomedicine (2018) Vol. 157, pp. 19-30
Closed Access | Times Cited: 407

Showing 76-100 of 407 citing articles:

Harnessing the power of radiomics and deep learning for improved breast cancer diagnosis with multiparametric breast mammography
Tariq Mahmood, Tanzila Saba, Amjad Rehman, et al.
Expert Systems with Applications (2024) Vol. 249, pp. 123747-123747
Closed Access | Times Cited: 13
Enhancing skin lesion classification with advanced deep learning ensemble models: a path towards accurate medical diagnostics
M. S. Kavitha, Sumathy Gnanagurusubbiah, Reena Roy Roby Roy, et al.
Current Problems in Cancer (2024) Vol. 49, pp. 101077-101077
Closed Access | Times Cited: 8
A smart tele-cytology point-of-care platform for oral cancer screening
Sumsum P. Sunny, Arun Baby, Bonney Lee James, et al.
PLoS ONE (2019) Vol. 14, Iss. 11, pp. e0224885-e0224885
Open Access | Times Cited: 70
Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review
Yuliana Jiménez-Gaona, María José Rodríguez-Álvarez, Vasudevan Lakshminarayanan
Applied Sciences (2020) Vol. 10, Iss. 22, pp. 8298-8298
Open Access | Times Cited: 68
Medical image based breast cancer diagnosis: State of the art and future directions
Mehreen Tariq, Sajid Iqbal, Hareem Ayesha, et al.
Expert Systems with Applications (2020) Vol. 167, pp. 114095-114095
Closed Access | Times Cited: 66
A Study of Fine-Tuning CNN Models Based on Thermal Imaging for Breast Cancer Classification
Roslidar Roslidar, Khairun Saddami, Fitri Arnia, et al.
(2019), pp. 77-81
Closed Access | Times Cited: 64
Artificial Neural Network Based Breast Cancer Screening: A Comprehensive Review
Subrato Bharati, Prajoy Podder, M. Rubaiyat Hossain Mondal
arXiv (Cornell University) (2020)
Open Access | Times Cited: 63
Breast Cancer Multi-classification through Deep Neural Network and Hierarchical Classification Approach
Ghulam Murtaza, Liyana Shuib, Ghulam Mujtaba, et al.
Multimedia Tools and Applications (2019) Vol. 79, Iss. 21-22, pp. 15481-15511
Closed Access | Times Cited: 59
A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms
Said Boumaraf, Xiabi Liu, Chokri Ferkous, et al.
BioMed Research International (2020) Vol. 2020, pp. 1-17
Open Access | Times Cited: 57
Deep learning algorithm for breast masses classification in mammograms
G. Suganthi, Sutha Joypaul, Parvathy Meenakshi Sundaram, et al.
IET Image Processing (2020) Vol. 14, Iss. 12, pp. 2860-2868
Open Access | Times Cited: 54
Breast cancer detection from histopathology images with deep inception and residual blocks
Shiksha Singh, Rajesh Kumar
Multimedia Tools and Applications (2021) Vol. 81, Iss. 4, pp. 5849-5865
Closed Access | Times Cited: 48
Breast cancer detection from histopathology images using modified residual neural networks
Varun Gupta, Megha Vasudev, Amit Doegar, et al.
Journal of Applied Biomedicine (2021) Vol. 41, Iss. 4, pp. 1272-1287
Closed Access | Times Cited: 43
Deep learning in breast radiology: current progress and future directions
William C. Ou, Dogan S. Polat, Başak E. Doğan
European Radiology (2021) Vol. 31, Iss. 7, pp. 4872-4885
Closed Access | Times Cited: 42
C-Net: A reliable convolutional neural network for biomedical image classification
Hosein Barzekar, Zeyun Yu
Expert Systems with Applications (2021) Vol. 187, pp. 116003-116003
Open Access | Times Cited: 42
Breast cancer classification on thermograms using deep CNN and transformers
Ella Mahoro, Moulay A. Akhloufi
Quantitative InfraRed Thermography Journal (2022) Vol. 21, Iss. 1, pp. 30-49
Closed Access | Times Cited: 31
The Systematic Review of Artificial Intelligence Applications in Breast Cancer Diagnosis
Dilber Uzun Ozsahin, Declan Ikechukwu Emegano, Berna Uzun, et al.
Diagnostics (2022) Vol. 13, Iss. 1, pp. 45-45
Open Access | Times Cited: 31
Patchless Multi-Stage Transfer Learning for Improved Mammographic Breast Mass Classification
Gelan Ayana, Jinhyung Park, Se‐woon Choe
Cancers (2022) Vol. 14, Iss. 5, pp. 1280-1280
Open Access | Times Cited: 28
A blockchain-enabled internet of medical things system for breast cancer detection in healthcare
Sushovan Chaudhury, Kartik Sau
Healthcare Analytics (2023) Vol. 4, pp. 100221-100221
Open Access | Times Cited: 19
Classification of Breast Cancer Using Transfer Learning and Advanced Al-Biruni Earth Radius Optimization
Amel Ali Alhussan, Abdelaziz A. Abdelhamid, S. K. Towfek, et al.
Biomimetics (2023) Vol. 8, Iss. 3, pp. 270-270
Open Access | Times Cited: 18
Development of an Artificial Intelligence-Based Breast Cancer Detection Model by Combining Mammograms and Medical Health Records
Nguyen Thi Hoang Trang, Khương Quỳnh Long, Pham Lê An, et al.
Diagnostics (2023) Vol. 13, Iss. 3, pp. 346-346
Open Access | Times Cited: 17
A Secure Internet of Medical Things Framework for Breast Cancer Detection in Sustainable Smart Cities
Theyazn H. H. Aldhyani, Mohammad Ayoub Khan, Mohammed Amin Almaiah, et al.
Electronics (2023) Vol. 12, Iss. 4, pp. 858-858
Open Access | Times Cited: 17
GABNet: global attention block for retinal OCT disease classification
Xuan Huang, Zhuang Ai, Hui Wang, et al.
Frontiers in Neuroscience (2023) Vol. 17
Open Access | Times Cited: 16
Fine-tuning pre-trained neural networks for medical image classification in small clinical datasets
Newton Spolaôr, Huei Lee, Ana Isabel Mendes, et al.
Multimedia Tools and Applications (2023) Vol. 83, Iss. 9, pp. 27305-27329
Closed Access | Times Cited: 16
A review of the machine learning datasets in mammography, their adherence to the FAIR principles and the outlook for the future
Joe Logan, Paul Kennedy, Daniel Catchpoole
Scientific Data (2023) Vol. 10, Iss. 1
Open Access | Times Cited: 16
Paradigm shift from Artificial Neural Networks (ANNs) to deep Convolutional Neural Networks (DCNNs) in the field of medical image processing
Serdar Abut, Hayrettin Okut, K. James Kallail
Expert Systems with Applications (2023) Vol. 244, pp. 122983-122983
Open Access | Times Cited: 16

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