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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:

Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review
Nisreen I. R. Yassin, Shaimaa Omran, Enas M. F. El Houby, et al.
Computer Methods and Programs in Biomedicine (2017) Vol. 156, pp. 25-45
Closed Access | Times Cited: 333

Showing 1-25 of 333 citing articles:

A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification
Mugahed A. Al-antari, Mohammed A. Al‐masni, Mun‐Taek Choi, et al.
International Journal of Medical Informatics (2018) Vol. 117, pp. 44-54
Closed Access | Times Cited: 367
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
Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges
Ghulam Murtaza, Liyana Shuib, Ainuddin Wahid Abdul Wahab, et al.
Artificial Intelligence Review (2019) Vol. 53, Iss. 3, pp. 1655-1720
Closed Access | Times Cited: 258
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
Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network
Vivek Kumar Singh, Hatem A. Rashwan, Santiago Romaní, et al.
Expert Systems with Applications (2019) Vol. 139, pp. 112855-112855
Open Access | Times Cited: 220
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
Prospective assessment of breast cancer risk from multimodal multiview ultrasound images via clinically applicable deep learning
Xuejun Qian, Jing Pei, Hui Zheng, et al.
Nature Biomedical Engineering (2021) Vol. 5, Iss. 6, pp. 522-532
Closed Access | Times Cited: 173
A CNN-based methodology for breast cancer diagnosis using thermal images
Juan Zuluaga-Gómez, Zeina Al Masry, Khaled Benaggoune, et al.
Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization (2020) Vol. 9, Iss. 2, pp. 131-145
Open Access | Times Cited: 161
A framework for breast cancer classification using Multi-DCNNs
Dina A. Ragab, Omneya Attallah, Maha Sharkas, et al.
Computers in Biology and Medicine (2021) Vol. 131, pp. 104245-104245
Open Access | Times Cited: 144
Deep Learning for Smart Healthcare—A Survey on Brain Tumor Detection from Medical Imaging
Mahsa Arabahmadi, Reza Farahbakhsh, Javad Rezazadeh
Sensors (2022) Vol. 22, Iss. 5, pp. 1960-1960
Open Access | Times Cited: 141
YOLO Based Breast Masses Detection and Classification in Full-Field Digital Mammograms
Ghada Hamed, Mohammed Marey, Safaa Amin El-Sayed, et al.
Computer Methods and Programs in Biomedicine (2020) Vol. 200, pp. 105823-105823
Closed Access | Times Cited: 140
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
Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction
Maged Nasser, Umi Kalsom Yusof
Diagnostics (2023) Vol. 13, Iss. 1, pp. 161-161
Open Access | Times Cited: 110
SMU-Net: Saliency-Guided Morphology-Aware U-Net for Breast Lesion Segmentation in Ultrasound Image
Zhenyuan Ning, Shengzhou Zhong, Qianjin Feng, et al.
IEEE Transactions on Medical Imaging (2021) Vol. 41, Iss. 2, pp. 476-490
Closed Access | Times Cited: 109
TTCNN: A Breast Cancer Detection and Classification towards Computer-Aided Diagnosis Using Digital Mammography in Early Stages
Sarmad Maqsood, Robertas Damaševičius, Rytis Maskeliūnas
Applied Sciences (2022) Vol. 12, Iss. 7, pp. 3273-3273
Open Access | Times Cited: 101
CSwin-PNet: A CNN-Swin Transformer combined pyramid network for breast lesion segmentation in ultrasound images
Haonan Yang, Dapeng Yang
Expert Systems with Applications (2022) Vol. 213, pp. 119024-119024
Closed Access | Times Cited: 91
Artificial intelligence for breast cancer analysis: Trends & directions
Shahid Munir Shah, Rizwan Ahmed Khan, Sheeraz Arif, et al.
Computers in Biology and Medicine (2022) Vol. 142, pp. 105221-105221
Open Access | Times Cited: 77
A Comprehensive Review on Breast Cancer Detection, Classification and Segmentation Using Deep Learning
Barsha Abhisheka, Saroj Kumar Biswas, Biswajit Purkayastha
Archives of Computational Methods in Engineering (2023) Vol. 30, Iss. 8, pp. 5023-5052
Closed Access | Times Cited: 64
A Comprehensive Survey on Federated Learning Techniques for Healthcare Informatics
Dasaradharami Reddy Kandati, Thippa Reddy Gadekallu
Computational Intelligence and Neuroscience (2023) Vol. 2023, Iss. 1
Open Access | Times Cited: 56
Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network
Hameedur Rahman, Tanvir Fatima Naik Bukht, Rozilawati Ahmad, et al.
Computational Intelligence and Neuroscience (2023) Vol. 2023, Iss. 1
Open Access | Times Cited: 43
Automated breast cancer detection in mammography using ensemble classifier and feature weighting algorithms
Fei Yan, Hesheng Huang, Witold Pedrycz, et al.
Expert Systems with Applications (2023) Vol. 227, pp. 120282-120282
Closed Access | Times Cited: 41
Breast cancer detection and diagnosis using hybrid deep learning architecture
R. Sathesh Raaj
Biomedical Signal Processing and Control (2023) Vol. 82, pp. 104558-104558
Closed Access | Times Cited: 40
Machine learning and deep learning techniques for breast cancer diagnosis and classification: a comprehensive review of medical imaging studies
Mehran Radak, Haider Yabr Lafta, Hossein Fallahi
Journal of Cancer Research and Clinical Oncology (2023) Vol. 149, Iss. 12, pp. 10473-10491
Closed Access | Times Cited: 40
Breast cancer detection using deep learning techniques: challenges and future directions
Muhammad Shahid, Azhar Imran
Multimedia Tools and Applications (2025)
Closed Access | Times Cited: 2
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