Michael J. Horry

681 total citations
8 papers, 378 citations indexed

About

Michael J. Horry is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Health Informatics. According to data from OpenAlex, Michael J. Horry has authored 8 papers receiving a total of 378 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Radiology, Nuclear Medicine and Imaging, 4 papers in Pulmonary and Respiratory Medicine and 3 papers in Health Informatics. Recurrent topics in Michael J. Horry's work include COVID-19 diagnosis using AI (7 papers), Radiomics and Machine Learning in Medical Imaging (5 papers) and Artificial Intelligence in Healthcare and Education (3 papers). Michael J. Horry is often cited by papers focused on COVID-19 diagnosis using AI (7 papers), Radiomics and Machine Learning in Medical Imaging (5 papers) and Artificial Intelligence in Healthcare and Education (3 papers). Michael J. Horry collaborates with scholars based in Australia, Malaysia and Saudi Arabia. Michael J. Horry's co-authors include Biswajeet Pradhan, Subrata Chakraborty, Manoranjan Paul, Anwaar Ulhaq, Nagesh Shukla, Manas Saha, Mansour Almazroui, U. Rajendra Acharya, Jing Zhu and Prabal Datta Barua and has published in prestigious journals such as IEEE Access, Sensors and Computers in Biology and Medicine.

In The Last Decade

Michael J. Horry

8 papers receiving 366 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Michael J. Horry Australia	5	316	207	62	55	53	8	378
Manas Saha India	4	288 0.9×	193 0.9×	52 0.8×	42 0.8×	66 1.2×	8	361
Lucas Teixeira Brazil	5	392 1.2×	280 1.4×	75 1.2×	77 1.4×	65 1.2×	15	457
Hayden Gunraj Canada	6	283 0.9×	190 0.9×	53 0.9×	38 0.7×	48 0.9×	11	317
Ecem Sogancioglu Netherlands	7	283 0.9×	112 0.5×	48 0.8×	122 2.2×	44 0.8×	10	379
Erdi Çallı Netherlands	6	270 0.9×	106 0.5×	54 0.9×	106 1.9×	34 0.6×	8	333
Huan Yuan China	4	486 1.5×	318 1.5×	130 2.1×	66 1.2×	79 1.5×	6	612
Chunli Qin China	4	211 0.7×	83 0.4×	23 0.4×	85 1.5×	31 0.6×	7	281
Preesat Biswas India	6	259 0.8×	192 0.9×	42 0.7×	26 0.5×	55 1.0×	16	328
Ziwang Huang China	4	511 1.6×	320 1.5×	109 1.8×	61 1.1×	78 1.5×	4	558
Zhanwei Xu China	5	360 1.1×	187 0.9×	118 1.9×	39 0.7×	38 0.7×	7	438

Countries citing papers authored by Michael J. Horry

Since Specialization

Citations

This map shows the geographic impact of Michael J. Horry'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 Michael J. Horry with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael J. Horry more than expected).

Fields of papers citing papers by Michael J. Horry

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael J. Horry. 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 Michael J. Horry. The network helps show where Michael J. Horry may publish in the future.

Co-authorship network of co-authors of Michael J. Horry

This figure shows the co-authorship network connecting the top 25 collaborators of Michael J. Horry. A scholar is included among the top collaborators of Michael J. Horry 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 Michael J. Horry. Michael J. Horry is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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8 of 8 papers shown

1.

Horry, Michael J., Subrata Chakraborty, Biswajeet Pradhan, et al.. (2023). Full-Resolution Lung Nodule Localization From Chest X-Ray Images Using Residual Encoder-Decoder Networks. IEEE Access. 11. 143016–143036. 1 indexed citations

2.

Horry, Michael J., et al.. (2023). Two-Speed Deep-Learning Ensemble for Classification of Incremental Land-Cover Satellite Image Patches. Earth Systems and Environment. 7(2). 525–540. 12 indexed citations

3.

Horry, Michael J., Subrata Chakraborty, Biswajeet Pradhan, et al.. (2023). Development of Debiasing Technique for Lung Nodule Chest X-ray Datasets to Generalize Deep Learning Models. Sensors. 23(14). 6585–6585. 6 indexed citations

4.

Chakraborty, Subrata, et al.. (2022). Generalizability assessment of COVID-19 3D CT data for deep learning-based disease detection. Computers in Biology and Medicine. 145. 105464–105464. 17 indexed citations

5.

Horry, Michael J.. (2021). Deep Mining Generation of Lung Cancer Malignancy Models from Chest X-ray Images. MDPI (MDPI AG). 12 indexed citations

6.

Horry, Michael J., et al.. (2021). Factors determining generalization in deep learning models for scoring COVID-CT images. Mathematical Biosciences & Engineering. 18(6). 9264–9293. 4 indexed citations

7.

Gomes, Douglas Pinto Sampaio, et al.. (2021). Features Of ICU Admission In X-Ray Images Of Covid-19 Patients. Victoria University Research Repository (Victoria University). 369. 200–204. 3 indexed citations

8.

Horry, Michael J., Subrata Chakraborty, Manoranjan Paul, et al.. (2020). COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data. IEEE Access. 8. 149808–149824. 323 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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