Marcia Hon

481 total citations
2 papers, 281 citations indexed

About

Marcia Hon is a scholar working on Neurology, Health Information Management and Computer Vision and Pattern Recognition. According to data from OpenAlex, Marcia Hon has authored 2 papers receiving a total of 281 indexed citations (citations by other indexed papers that have themselves been cited), including 2 papers in Neurology, 1 paper in Health Information Management and 1 paper in Computer Vision and Pattern Recognition. Recurrent topics in Marcia Hon's work include Brain Tumor Detection and Classification (2 papers), Medical Imaging and Analysis (1 paper) and Artificial Intelligence in Healthcare (1 paper). Marcia Hon is often cited by papers focused on Brain Tumor Detection and Classification (2 papers), Medical Imaging and Analysis (1 paper) and Artificial Intelligence in Healthcare (1 paper). Marcia Hon collaborates with scholars based in Canada. Marcia Hon's co-authors include Naimul Khan and Nabila Abraham and has published in prestigious journals such as IEEE Access.

In The Last Decade

Marcia Hon

2 papers receiving 267 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Marcia Hon Canada	2	182	133	74	70	70	2	281
Nasrin M. Makbol Yemen	4	187 1.0×	118 0.9×	59 0.8×	69 1.0×	58 0.8×	7	288
Akshay Aggarwal India	6	209 1.1×	149 1.1×	78 1.1×	58 0.8×	84 1.2×	16	357
M. Satya Sai Ram India	7	164 0.9×	86 0.6×	52 0.7×	50 0.7×	82 1.2×	40	276
Farheen Ramzan Pakistan	5	180 1.0×	136 1.0×	69 0.9×	57 0.8×	96 1.4×	7	387
Sergey Korolev Russia	3	167 0.9×	127 1.0×	82 1.1×	24 0.3×	78 1.1×	4	278
Yulia Dodonova Russia	3	167 0.9×	128 1.0×	82 1.1×	24 0.3×	78 1.1×	5	281
Rajeswari India	5	125 0.7×	67 0.5×	55 0.7×	51 0.7×	67 1.0×	19	252
Wenyong Zhu China	4	163 0.9×	168 1.3×	74 1.0×	40 0.6×	84 1.2×	5	334
Yechong Huang China	5	150 0.8×	111 0.8×	86 1.2×	23 0.3×	73 1.0×	6	289
Yogesh Kumar Rathore India	9	96 0.5×	61 0.5×	48 0.6×	49 0.7×	74 1.1×	29	278

Countries citing papers authored by Marcia Hon

Since Specialization

Citations

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

Fields of papers citing papers by Marcia Hon

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Marcia Hon. 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 Marcia Hon. The network helps show where Marcia Hon may publish in the future.

Co-authorship network of co-authors of Marcia Hon

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

All Works

Sort: Min cites: Since: Top N: Style:

2 of 2 papers shown

1.

Khan, Naimul, Nabila Abraham, & Marcia Hon. (2019). Transfer Learning With Intelligent Training Data Selection for Prediction of Alzheimer’s Disease. IEEE Access. 7. 72726–72735. 126 indexed citations

2.

Hon, Marcia & Naimul Khan. (2017). Towards Alzheimer's disease classification through transfer learning. 1166–1169. 155 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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