Optimal deep learning model for classification of lung cancer on CT images
Impact in
Classified as
In The Last Decade
doi.org/10.1016/j.future.2018.10.009 →Countries where authors are citing Optimal deep learning model for classification of lung cancer on CT images
This map shows the geographic impact of Optimal deep learning model for classification of lung cancer on CT images. 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 Optimal deep learning model for classification of lung cancer on CT images with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Optimal deep learning model for classification of lung cancer on CT images more than expected).
Fields of papers citing Optimal deep learning model for classification of lung cancer on CT images
This network shows the impact of Optimal deep learning model for classification of lung cancer on CT images. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Optimal deep learning model for classification of lung cancer on CT images.
About Optimal deep learning model for classification of lung cancer on CT images
This paper, published in 2018, received 405 indexed citations . Written by Sachi Nandan Mohanty, K. Shankar, N. Arunkumar and Gustavo Ramírez-González covering the research area of Neurology and Radiology, Nuclear Medicine and Imaging. It is primarily cited by scholars working on Radiology, Nuclear Medicine and Imaging (283 citations), Artificial Intelligence (184 citations), Pulmonary and Respiratory Medicine (176 citations), Computer Vision and Pattern Recognition (56 citations) and Neurology (52 citations). Published in Future Generation Computer Systems.
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.
This paper is also available at doi.org/10.1016/j.future.2018.10.009.