Reda Kasmi

717 total citations
11 papers, 514 citations indexed

About

Reda Kasmi is a scholar working on Oncology, Artificial Intelligence and Epidemiology. According to data from OpenAlex, Reda Kasmi has authored 11 papers receiving a total of 514 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Oncology, 7 papers in Artificial Intelligence and 5 papers in Epidemiology. Recurrent topics in Reda Kasmi's work include Cutaneous Melanoma Detection and Management (11 papers), AI in cancer detection (7 papers) and Nonmelanoma Skin Cancer Studies (5 papers). Reda Kasmi is often cited by papers focused on Cutaneous Melanoma Detection and Management (11 papers), AI in cancer detection (7 papers) and Nonmelanoma Skin Cancer Studies (5 papers). Reda Kasmi collaborates with scholars based in Algeria, United States and France. Reda Kasmi's co-authors include Karim Mokrani, William V. Stoecker, Jason Hagerty, R. Joe Stanley, Hocine Cherifi, Harold Rabinovitz, Haidar Almubarak, Margaret Oliviero, Rhett J Drugge and Peng Guo and has published in prestigious journals such as IEEE Journal of Biomedical and Health Informatics, Optik and Skin Research and Technology.

In The Last Decade

Reda Kasmi

11 papers receiving 491 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Reda Kasmi	429	333	125	77	70	11	514
Roberta B. Oliveira	388 0.9×	292 0.9×	122 1.0×	125 1.6×	73 1.0×	22	540
Jason Hagerty	273 0.6×	220 0.7×	94 0.8×	76 1.0×	52 0.7×	26	402
Joseph M. Malters	447 1.0×	260 0.8×	130 1.0×	82 1.1×	139 2.0×	13	546
Nabin K. Mishra	305 0.7×	250 0.8×	115 0.9×	58 0.8×	50 0.7×	8	403
Jinman Kim	312 0.7×	270 0.8×	117 0.9×	124 1.6×	70 1.0×	12	483
Aadi Kalloo	317 0.7×	264 0.8×	133 1.1×	42 0.5×	41 0.6×	10	434
Ammara Masood	268 0.6×	235 0.7×	76 0.6×	66 0.9×	46 0.7×	13	366
Ebrahim Nasr-Esfahani	292 0.7×	263 0.8×	92 0.7×	154 2.0×	68 1.0×	10	527
Mariana Francisco	274 0.6×	218 0.7×	75 0.6×	58 0.8×	40 0.6×	5	337
Amirreza Mahbod	548 1.3×	549 1.6×	232 1.9×	199 2.6×	83 1.2×	19	870

Countries citing papers authored by Reda Kasmi

Since Specialization

Citations

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

Fields of papers citing papers by Reda Kasmi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Reda Kasmi

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

All Works

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11 of 11 papers shown

Kasmi, Reda, et al.. (2023). SharpRazor: Automatic removal of hair and ruler marks from dermoscopy images. Skin Research and Technology. 29(4). e13203–e13203. 8 indexed citations

Kasmi, Reda, et al.. (2022). ChimeraNet: U-Net for Hair Detection in Dermoscopic Skin Lesion Images. Journal of Digital Imaging. 36(2). 526–535. 18 indexed citations

Kasmi, Reda, et al.. (2021). Automatic segmentation and melanoma detection based on color and texture features in dermoscopic images. Skin Research and Technology. 28(2). 203–211. 19 indexed citations

Mishra, Nabin K., Ravneet Kaur, Reda Kasmi, et al.. (2019). Automatic lesion border selection in dermoscopy images using morphology and color features. Skin Research and Technology. 25(4). 544–552. 13 indexed citations

Hagerty, Jason, R. Joe Stanley, Haidar Almubarak, et al.. (2019). Deep Learning and Handcrafted Method Fusion: Higher Diagnostic Accuracy for Melanoma Dermoscopy Images. IEEE Journal of Biomedical and Health Informatics. 23(4). 1385–1391. 145 indexed citations

Mishra, Nabin K., Ravneet Kaur, Reda Kasmi, et al.. (2017). Automatic Separation of Basal Cell Carcinoma from Benign Lesions in Dermoscopy Images with Border Thresholding Techniques. 115–123. 3 indexed citations

Kasmi, Reda, et al.. (2017). Segmentation and classification of melanoma and benign skin lesions. Optik. 140. 749–761. 68 indexed citations

Kaur, Ravneet, Nabin K. Mishra, Jason Hagerty, et al.. (2016). Thresholding methods for lesion segmentation of basal cell carcinoma in dermoscopy images. Skin Research and Technology. 23(3). 416–428. 14 indexed citations

Kaur, Ravneet, et al.. (2016). Adaptable texture‐based segmentation by variance and intensity for automatic detection of semitranslucent and pink blush areas in basal cell carcinoma. Skin Research and Technology. 22(4). 412–422. 11 indexed citations

10.

Kasmi, Reda & Karim Mokrani. (2016). Classification of malignant melanoma and benign skin lesions: implementation of automatic ABCD rule. IET Image Processing. 10(6). 448–455. 177 indexed citations

11.

Kasmi, Reda, et al.. (2015). Biologically inspired skin lesion segmentation using a geodesic active contour technique. Skin Research and Technology. 22(2). 208–222. 38 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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