Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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 1-25 of 407 citing articles:

Transfer learning for medical image classification: a literature review
Kim Eun Hee, Alejandro Cosa‐Linan, Nandhini Santhanam, et al.
BMC Medical Imaging (2022) Vol. 22, Iss. 1
Open Access | Times Cited: 480
Optimal deep learning model for classification of lung cancer on CT images
Lakshmanaprabu S.K., Sachi Nandan Mohanty, K. Shankar, et al.
Future Generation Computer Systems (2018) Vol. 92, pp. 374-382
Closed Access | Times Cited: 446
Big Data and Artificial Intelligence Modeling for Drug Discovery
Hao Zhu
The Annual Review of Pharmacology and Toxicology (2019) Vol. 60, Iss. 1, pp. 573-589
Open Access | Times Cited: 353
Adam Optimization Algorithm for Wide and Deep Neural Network
Imran Khan Mohd Jais, Amelia Ritahani Ismail, Syed Qamrun Nisa
Knowledge Engineering and Data Science (2019) Vol. 2, Iss. 1, pp. 41-41
Open Access | Times Cited: 332
Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review
Essam H. Houssein, Marwa M. Emam, Abdelmgeid A. Ali, et al.
Expert Systems with Applications (2020) Vol. 167, pp. 114161-114161
Closed Access | Times Cited: 307
Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification
Mohammed A. Al‐masni, Dong‐Hyun Kim, Tae‐Seong Kim
Computer Methods and Programs in Biomedicine (2020) Vol. 190, pp. 105351-105351
Closed Access | Times Cited: 287
Artificial intelligence in breast imaging
Elizabeth Le, Yanzhong Wang, Yuan Huang, et al.
Clinical Radiology (2019) Vol. 74, Iss. 5, pp. 357-366
Open Access | Times Cited: 243
Deep convolutional neural networks for mammography: advances, challenges and applications
Dina Abdelhafiz, Clifford Yang, Reda A. Ammar, et al.
BMC Bioinformatics (2019) Vol. 20, Iss. S11
Open Access | Times Cited: 224
Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital X-ray mammograms
Mugahed A. Al-antari, Seung‐Moo Han, Tae‐Seong Kim
Computer Methods and Programs in Biomedicine (2020) Vol. 196, pp. 105584-105584
Closed Access | Times Cited: 220
Medical image analysis based on deep learning approach
Murali Krishna Puttagunta, S. Ravi
Multimedia Tools and Applications (2021)
Open Access | Times Cited: 206
Digital Breast Tomosynthesis: Concepts and Clinical Practice
Alice Chong, Susan P. Weinstein, Elizabeth S. McDonald, et al.
Radiology (2019) Vol. 292, Iss. 1, pp. 1-14
Open Access | Times Cited: 198
A Review of Deep Learning on Medical Image Analysis
Jian Wang, Hengde Zhu, Shuihua Wang‎, et al.
Mobile Networks and Applications (2020) Vol. 26, Iss. 1, pp. 351-380
Closed Access | Times Cited: 188
A hybrid classification autoencoder for semi-supervised fault diagnosis in rotating machinery
Xinya Wu, Yan Zhang, Changming Cheng, et al.
Mechanical Systems and Signal Processing (2020) Vol. 149, pp. 107327-107327
Closed Access | Times Cited: 185
Diagnosis of Benign and Malignant Breast Lesions on DCE‐MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue
Jiejie Zhou, Yang Zhang, Kai‐Ting Chang, et al.
Journal of Magnetic Resonance Imaging (2019) Vol. 51, Iss. 3, pp. 798-809
Open Access | Times Cited: 169
Automatic mass detection in mammograms using deep convolutional neural networks
Richa Agarwal, Oliver Díaz, Xavier Lladó, et al.
Journal of Medical Imaging (2019) Vol. 6, Iss. 03, pp. 1-1
Open Access | Times Cited: 158
Transfer Learning With Adaptive Fine-Tuning
Grega Vrbančič, Vili Podgorelec
IEEE Access (2020) Vol. 8, pp. 196197-196211
Open Access | Times Cited: 157
Recognition of peripheral blood cell images using convolutional neural networks
Andrea Acevedo, Santiago Alférez, Anna Merino, et al.
Computer Methods and Programs in Biomedicine (2019) Vol. 180, pp. 105020-105020
Closed Access | Times Cited: 155
Attention Dense-U-Net for Automatic Breast Mass Segmentation in Digital Mammogram
Shuyi Li, Min Dong, Guangming Du, et al.
IEEE Access (2019) Vol. 7, pp. 59037-59047
Open Access | Times Cited: 154
Hourly energy consumption prediction of an office building based on ensemble learning and energy consumption pattern classification
Zhenxiang Dong, Jiangyan Liu, Bin Liu, et al.
Energy and Buildings (2021) Vol. 241, pp. 110929-110929
Closed Access | Times Cited: 153
CAD and AI for breast cancer—recent development and challenges
Heang‐Ping Chan, Ravi K. Samala, Lubomir M. Hadjiiski
British Journal of Radiology (2019) Vol. 93, Iss. 1108
Open Access | Times Cited: 145
Prediction of HER2-positive breast cancer recurrence and metastasis risk from histopathological images and clinical information via multimodal deep learning
Jialiang Yang, Jie Ju, Lei Guo, et al.
Computational and Structural Biotechnology Journal (2021) Vol. 20, pp. 333-342
Open Access | Times Cited: 144
Transfer learning for medical images analyses: A survey
Xiang Yu, Jian Wang, Qingqi Hong, et al.
Neurocomputing (2022) Vol. 489, pp. 230-254
Closed Access | Times Cited: 138
Convolutional neural networks for breast cancer detection in mammography: A survey
Leila Abdelrahman, Manal Alghamdi, Fernando Collado‐Mesa, et al.
Computers in Biology and Medicine (2021) Vol. 131, pp. 104248-104248
Closed Access | Times Cited: 115
Survey on Machine Learning and Deep Learning Applications in Breast Cancer Diagnosis
Gunjan Chugh, Shailender Kumar, Nanhay Singh
Cognitive Computation (2021) Vol. 13, Iss. 6, pp. 1451-1470
Closed Access | Times Cited: 111
Automated diagnosis of breast cancer using multi-modal datasets: A deep convolution neural network based approach
Debendra Muduli, Ratnakar Dash, Banshidhar Majhi
Biomedical Signal Processing and Control (2021) Vol. 71, pp. 102825-102825
Closed Access | Times Cited: 111
Page 1 - Next Page

Scroll to top