Gopichandh Danala

1.3k total citations · 1 hit paper
35 papers, 654 citations indexed

About

Gopichandh Danala is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Gopichandh Danala has authored 35 papers receiving a total of 654 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Radiology, Nuclear Medicine and Imaging, 16 papers in Artificial Intelligence and 9 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Gopichandh Danala's work include Radiomics and Machine Learning in Medical Imaging (17 papers), AI in cancer detection (16 papers) and Digital Radiography and Breast Imaging (4 papers). Gopichandh Danala is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (17 papers), AI in cancer detection (16 papers) and Digital Radiography and Breast Imaging (4 papers). Gopichandh Danala collaborates with scholars based in United States and Iran. Gopichandh Danala's co-authors include Bin Zheng, Morteza Heidari, Seyedehnafiseh Mirniaharikandehei, Yuchen Qiu, Abolfazl Zargari Khuzani, Hong Liu, S. Lakshmivarahan, Teresa Wu, Bhavika Patel and Jing Li and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and IEEE Transactions on Biomedical Engineering.

In The Last Decade

Gopichandh Danala

35 papers receiving 638 citations

Hit Papers

Improving the performance of CNN to predict the likelihoo... 2020 2026 2022 2024 2020 50 100 150 200 250
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms
    2020 290 citations Morteza Heidari, Seyedehnafiseh Mirniaharikandehei et al. International Journal of Medical Informatics profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Gopichandh Danala United States 11 447 342 123 88 54 35 654
Clifford Yang United States 11 377 0.8× 356 1.0× 85 0.7× 87 1.0× 63 1.2× 22 673
Dooman Arefan United States 14 418 0.9× 273 0.8× 138 1.1× 42 0.5× 80 1.5× 43 597
Morteza Heidari United States 10 562 1.3× 429 1.3× 132 1.1× 132 1.5× 52 1.0× 35 759
Carson Lam United States 13 606 1.4× 275 0.8× 106 0.9× 179 2.0× 38 0.7× 25 1.1k
Christoph Haarburger Germany 11 446 1.0× 310 0.9× 77 0.6× 105 1.2× 48 0.9× 16 676
Matteo Interlenghi Italy 15 577 1.3× 280 0.8× 171 1.4× 76 0.9× 67 1.2× 29 986
Xuxin Chen United States 11 400 0.9× 307 0.9× 129 1.0× 157 1.8× 57 1.1× 40 858
Mohamed Shehata Egypt 14 377 0.8× 152 0.4× 142 1.2× 122 1.4× 23 0.4× 55 648
Paul Desbordes France 9 285 0.6× 142 0.4× 104 0.8× 41 0.5× 43 0.8× 13 534

Countries citing papers authored by Gopichandh Danala

Since Specialization
Citations

This map shows the geographic impact of Gopichandh Danala's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Gopichandh Danala with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gopichandh Danala more than expected).

Fields of papers citing papers by Gopichandh Danala

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gopichandh Danala. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Gopichandh Danala. The network helps show where Gopichandh Danala may publish in the future.

Co-authorship network of co-authors of Gopichandh Danala

This figure shows the co-authorship network connecting the top 25 collaborators of Gopichandh Danala. A scholar is included among the top collaborators of Gopichandh Danala based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Gopichandh Danala. Gopichandh Danala is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Danala, Gopichandh, et al.. (2025). SMART 2.0: Social Media Analytics and Reporting Tool Applied to Misinformation Tracking. Media and Communication. 13. 1 indexed citations
2.
Devegowda, Deepak, et al.. (2025). An improved data-driven method for the prediction of elastic properties in unconventional shales from SEM images. Geoenergy Science and Engineering. 254. 214043–214043. 1 indexed citations
3.
Wagle, Pradeep, Gopichandh Danala, Catherine Donner, et al.. (2024). Modeling time series of vegetation indices in tallgrass prairie using machine and deep learning algorithms. Ecological Informatics. 84. 102917–102917. 4 indexed citations
4.
Danala, Gopichandh, et al.. (2022). A Comparison of Computer-Aided Diagnosis Schemes Optimized Using Radiomics and Deep Transfer Learning Methods. Bioengineering. 9(6). 256–256. 22 indexed citations
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Heidari, Morteza, S. Lakshmivarahan, Seyedehnafiseh Mirniaharikandehei, et al.. (2021). Applying a Random Projection Algorithm to Optimize Machine Learning Model for Breast Lesion Classification. IEEE Transactions on Biomedical Engineering. 68(9). 2764–2775. 19 indexed citations
8.
Mirniaharikandehei, Seyedehnafiseh, Morteza Heidari, Gopichandh Danala, S. Lakshmivarahan, & Bin Zheng. (2021). Applying a random projection algorithm to optimize machine learning model for predicting peritoneal metastasis in gastric cancer patients using CT images. Computer Methods and Programs in Biomedicine. 200. 105937–105937. 37 indexed citations
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Mirniaharikandehei, Seyedehnafiseh, Morteza Heidari, Gopichandh Danala, S. Lakshmivarahan, & Bin Zheng. (2021). A novel feature reduction method to improve performance of machine learning model. 76–76. 3 indexed citations
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Heidari, Morteza, Seyedehnafiseh Mirniaharikandehei, Abolfazl Zargari Khuzani, et al.. (2020). Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms. International Journal of Medical Informatics. 144. 104284–104284. 290 indexed citations breakdown →
14.
O’Connor, Kyle P., Gopichandh Danala, Chao Xu, et al.. (2019). Predicting Clinical Outcome After Mechanical Thrombectomy: The GADIS (Gender, Age, Diabetes Mellitus History, Infarct Volume, and Sex) Score. World Neurosurgery. 134. e1130–e1142. 13 indexed citations
15.
Zheng, Bin, et al.. (2019). Developing global image feature analysis models to predict cancer risk and prognosis. SHILAP Revista de lepidopterología. 2(1). 17–17. 5 indexed citations
16.
Mirniaharikandehei, Seyedehnafiseh, et al.. (2019). Developing a Quantitative Ultrasound Image Feature Analysis Scheme to Assess Tumor Treatment Efficacy Using a Mouse Model. Scientific Reports. 9(1). 7293–7293. 11 indexed citations
17.
Ray, Bappaditya, Stephen R. Ross, Gopichandh Danala, et al.. (2019). Systemic response of coated-platelet and peripheral blood inflammatory cell indices after aneurysmal subarachnoid hemorrhage and long-term clinical outcome. Journal of Critical Care. 52. 1–9. 25 indexed citations
18.
Danala, Gopichandh, Bhavika Patel, Morteza Heidari, et al.. (2018). Classification of Breast Masses Using a Computer-Aided Diagnosis Scheme of Contrast Enhanced Digital Mammograms. Annals of Biomedical Engineering. 46(9). 1419–1431. 58 indexed citations
19.
Danala, Gopichandh, Theresa Thai, Camille C. Gunderson, et al.. (2017). Applying Quantitative CT Image Feature Analysis to Predict Response of Ovarian Cancer Patients to Chemotherapy. Academic Radiology. 24(10). 1233–1239. 36 indexed citations
20.
Danala, Gopichandh, Yunzhi Wang, Theresa Thai, et al.. (2017). Apply radiomics approach for early stage prognostic evaluation of ovarian cancer patients: a preliminary study. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 10134. 1013449–1013449. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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