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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:

Automatic identification of malaria and other red blood cell inclusions using convolutional neural networks
Ángel Molina, José Rodellar, Laura Boldú, et al.
Computers in Biology and Medicine (2021) Vol. 136, pp. 104680-104680
Closed Access | Times Cited: 42

Showing 1-25 of 42 citing articles:

Parallel CNN-ELM: A multiclass classification of chest X-ray images to identify seventeen lung diseases including COVID-19
Md. Nahiduzzaman, Md. Omaer Faruq Goni, Rakibul Hassan, et al.
Expert Systems with Applications (2023) Vol. 229, pp. 120528-120528
Open Access | Times Cited: 34
DLRFNet: deep learning with random forest network for classification and detection of malaria parasite in blood smear
Anita Murmu, Piyush Kumar
Multimedia Tools and Applications (2024) Vol. 83, Iss. 23, pp. 63593-63615
Closed Access | Times Cited: 11
Supporting Malaria Diagnosis Using Deep Learning and Data Augmentation
Kenia Hoyos, William Hoyos
Diagnostics (2024) Vol. 14, Iss. 7, pp. 690-690
Open Access | Times Cited: 11
A vision transformer based approach for analysis of plasmodium vivax life cycle for malaria prediction using thin blood smear microscopic images
Neha Sengar, Radim Bürget, Malay Kishore Dutta
Computer Methods and Programs in Biomedicine (2022) Vol. 224, pp. 106996-106996
Closed Access | Times Cited: 28
Enhancing medical image analysis with unsupervised domain adaptation approach across microscopes and magnifications
Talha Ilyas, Khubaib Ahmad, Dewa Made Sri Arsa, et al.
Computers in Biology and Medicine (2024) Vol. 170, pp. 108055-108055
Open Access | Times Cited: 7
Automatic normalized digital color staining in the recognition of abnormal blood cells using generative adversarial networks
Kevin Barrera, José Rodellar, Santiago Alférez, et al.
Computer Methods and Programs in Biomedicine (2023) Vol. 240, pp. 107629-107629
Open Access | Times Cited: 13
Identification of Anemia and Its Severity Level in a Peripheral Blood Smear Using 3-Tier Deep Neural Network
Muhammad Shahzad, Arif Iqbal Umar, Syed Hamad Shirazi, et al.
Applied Sciences (2022) Vol. 12, Iss. 10, pp. 5030-5030
Open Access | Times Cited: 20
Malaria parasitic detection using a new Deep Boosted and Ensemble Learning framework
Hafiz M. Asif, Saddam Hussain Khan, Tahani Jaser Alahmadi, et al.
Complex & Intelligent Systems (2024) Vol. 10, Iss. 4, pp. 4835-4851
Open Access | Times Cited: 4
Application of image recognition technology in pathological diagnosis of blood smears
Wangxinjun Cheng, Jingshuang Liu, Chaofeng Wang, et al.
Clinical and Experimental Medicine (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 4
Computer-Aided Diagnosis Systems for Automatic Malaria Parasite Detection and Classification: A Systematic Review
Flavia Grignaffini, Patrizio Simeoni, Anna Alisi, et al.
Electronics (2024) Vol. 13, Iss. 16, pp. 3174-3174
Open Access | Times Cited: 4
VL-M2C: Leveraging deep learning approach for stage detection of malaria parasites
Gunjan Aggarwal, Mayank Kumar Goyal
Journal of Integrated Science and Technology (2025) Vol. 13, Iss. 3
Closed Access
Deep learning method for malaria parasite evaluation from microscopic blood smear
Abhinav Dahiya, Devvrat Raghuvanshi, Chhaya Sharma, et al.
Artificial Intelligence in Medicine (2025), pp. 103114-103114
Closed Access
MozzieNet: A deep learning approach to efficiently detect malaria parasites in blood smear images
Sohaib Asif, Saif Ur Rehman Khan, Xiaolong Zheng, et al.
International Journal of Imaging Systems and Technology (2023) Vol. 34, Iss. 1
Closed Access | Times Cited: 10
An MIoT Framework of Consumer Technology for Medical Diseases Prediction
Sudeshna Pattanaik, Chinmay Chakraborty, Subhasikta Behera, et al.
IEEE Transactions on Consumer Electronics (2024) Vol. 70, Iss. 1, pp. 3754-3761
Closed Access | Times Cited: 3
Machine and deep learning methods in identifying malaria through microscopic blood smear: A systematic review
Dhevisha Sukumarran, Khairunnisa Hasikin‬, Anis Salwa Mohd Khairuddin, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 133, pp. 108529-108529
Closed Access | Times Cited: 2
Morphology classification of malaria infected red blood cells using deep learning techniques
Fatima Abdullahi Muhammad, Rubita Sudirman, Nor Aini Zakaria, et al.
Biomedical Signal Processing and Control (2024) Vol. 99, pp. 106869-106869
Closed Access | Times Cited: 2
Application of Deep Learning in Clinical Settings for Detecting and Classifying Malaria Parasites in Thin Blood Smears
Geng Wang, Guoju Luo, Heqing Lian, et al.
Open Forum Infectious Diseases (2023) Vol. 10, Iss. 11
Open Access | Times Cited: 6
Performance of the new MC‐80 automated digital cell morphology analyser in detection of normal and abnormal blood cells: Comparison with the CellaVision DM9600
Anna Merino, Javier Laguna, María Rodríguez‐García, et al.
International Journal of Laboratory Hematology (2023) Vol. 46, Iss. 1, pp. 72-82
Open Access | Times Cited: 6
Diagnostic accuracy of fluorescence flow-cytometry technology using Sysmex XN-31 for imported malaria in a non-endemic setting
Stéphane Picot, Thomas Perpoint, Christian Chidiac, et al.
Parasite (2022) Vol. 29, pp. 31-31
Open Access | Times Cited: 9
An automated malaria cells detection from thin blood smear images using deep learning
Dhevisha Sukumarran
Tropical biomedicine (2023) Vol. 40, Iss. 2, pp. 208-219
Open Access | Times Cited: 5
Multiclass malaria parasite recognition based on transformer models and a generative adversarial network
Dianhuan Tan, Xianghui Liang
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 5
A Deep Learning Approach for the Morphological Recognition of Reactive Lymphocytes in Patients with COVID-19 Infection
José Rodellar, Kevin Barrera, Santiago Alférez, et al.
Bioengineering (2022) Vol. 9, Iss. 5, pp. 229-229
Open Access | Times Cited: 6
Deep Learning-Based Cell Detection and Extraction in Thin Blood Smears for Malaria Diagnosis
Deniz Kavzak Ufuktepe, Feng Yang, Yasmin M. Kassim, et al.
(2021), pp. 1-6
Open Access | Times Cited: 8
Diagnosing malaria with AI and image processing
Mogalraj Kushal Dath, Nahida Nazir
(2023), pp. 1-6
Closed Access | Times Cited: 2
Deep Learning based Web App for Malaria Parasite Detection in Granular Blood Samples
K. Santoshi, G. Saranya, Ch.Rama Reddy, et al.
(2023), pp. 291-297
Closed Access | Times Cited: 2
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