Jainy Sachdeva

856 total citations
19 papers, 595 citations indexed

About

Jainy Sachdeva is a scholar working on Computer Vision and Pattern Recognition, Neurology and Artificial Intelligence. According to data from OpenAlex, Jainy Sachdeva has authored 19 papers receiving a total of 595 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Computer Vision and Pattern Recognition, 12 papers in Neurology and 4 papers in Artificial Intelligence. Recurrent topics in Jainy Sachdeva's work include Brain Tumor Detection and Classification (12 papers), Medical Image Segmentation Techniques (8 papers) and Digital Imaging for Blood Diseases (6 papers). Jainy Sachdeva is often cited by papers focused on Brain Tumor Detection and Classification (12 papers), Medical Image Segmentation Techniques (8 papers) and Digital Imaging for Blood Diseases (6 papers). Jainy Sachdeva collaborates with scholars based in India. Jainy Sachdeva's co-authors include Chirag Ahuja, Niranjan Khandelwal, Indra Gupta, Vinod Kumar, Puneet Mishra, Deeksha Katoch and Vinod Kumar and has published in prestigious journals such as Applied Soft Computing, Image and Vision Computing and Magnetic Resonance Imaging.

In The Last Decade

Jainy Sachdeva

19 papers receiving 560 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jainy Sachdeva India 11 409 390 162 126 87 19 595
E. Montseny Spain 9 481 1.2× 369 0.9× 144 0.9× 130 1.0× 67 0.8× 40 698
Nelly Gordillo Mexico 5 360 0.9× 364 0.9× 85 0.5× 124 1.0× 33 0.4× 12 511
Matthew C. Clark United States 5 422 1.0× 265 0.7× 150 0.9× 120 1.0× 68 0.8× 6 538
Shuangliang Cao China 6 435 1.1× 482 1.2× 264 1.6× 177 1.4× 32 0.4× 11 639
Sarmad Maqsood Lithuania 11 310 0.8× 216 0.6× 293 1.8× 225 1.8× 161 1.9× 20 701
Vivek Singh Verma India 11 523 1.3× 214 0.5× 79 0.5× 82 0.7× 66 0.8× 21 621
Nilesh Bhaskarrao Bahadure India 7 398 1.0× 477 1.2× 173 1.1× 105 0.8× 39 0.4× 31 648
Mohamed Ben Slima Tunisia 11 262 0.6× 260 0.7× 113 0.7× 126 1.0× 28 0.3× 48 490
T. Kalaiselvi India 12 366 0.9× 316 0.8× 108 0.7× 112 0.9× 43 0.5× 49 518

Countries citing papers authored by Jainy Sachdeva

Since Specialization
Citations

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

Fields of papers citing papers by Jainy Sachdeva

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jainy Sachdeva

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

All Works

19 of 19 papers shown
1.
Sachdeva, Jainy, Puneet Mishra, & Deeksha Katoch. (2024). Diabetic retinopathy data augmentation and vessel segmentation through deep learning based three fully convolution neural networks. Image and Vision Computing. 151. 105284–105284. 2 indexed citations
2.
Sachdeva, Jainy, et al.. (2024). Multiscale segmentation net for segregating heterogeneous brain tumors: Gliomas on multimodal MR images. Image and Vision Computing. 149. 105191–105191. 6 indexed citations
3.
Sachdeva, Jainy, et al.. (2024). A Novel Hybrid Multimodal Medical Image Fusion Scheme Based on Non-subsampled Shearlet Transform. Circuits Systems and Signal Processing. 43(6). 3627–3648. 3 indexed citations
4.
Sachdeva, Jainy, et al.. (2024). Comparative Analysis of Different Deep Convolutional Neural Network Architectures for Classification of Brain Tumor on Magnetic Resonance Images. Archives of Computational Methods in Engineering. 31(4). 1959–1978. 19 indexed citations
5.
Sachdeva, Jainy, et al.. (2024). Resolving autism spectrum disorder (ASD) through brain topologies using fMRI dataset with multi-layer perceptron (MLP). Psychiatry Research Neuroimaging. 343. 111858–111858. 4 indexed citations
6.
Sachdeva, Jainy, et al.. (2024). Efficient‐Residual Net—A Hybrid Neural Network for Automated Brain Tumor Detection. International Journal of Imaging Systems and Technology. 34(5). 2 indexed citations
7.
8.
Sachdeva, Jainy, et al.. (2022). Multimodal Medical Image Fusion Using Nonsubsampled Shearlet Transform and Smallest Uni-Value Segment Assimilating Nucleus. International Journal of Pattern Recognition and Artificial Intelligence. 36(4). 6 indexed citations
9.
Sachdeva, Jainy, et al.. (2022). Enc‐Unet: A novel method for Glioma segmentation. International Journal of Imaging Systems and Technology. 33(2). 465–482. 5 indexed citations
10.
Sachdeva, Jainy, et al.. (2019). An improved multimodal medical image fusion scheme based on hybrid combination of nonsubsampled contourlet transform and stationary wavelet transform. International Journal of Imaging Systems and Technology. 29(2). 146–160. 33 indexed citations
11.
Sachdeva, Jainy, et al.. (2018). Multimodal medical image fusion using non-subsampled shearlet transform and pulse coupled neural network incorporated with morphological gradient. Signal Image and Video Processing. 12(8). 1479–1487. 36 indexed citations
12.
Sachdeva, Jainy, Vinod Kumar, Indra Gupta, Niranjan Khandelwal, & Chirag Ahuja. (2016). A package-SFERCB-“Segmentation, feature extraction, reduction and classification analysis by both SVM and ANN for brain tumors”. Applied Soft Computing. 47. 151–167. 103 indexed citations
13.
Sachdeva, Jainy, et al.. (2016). Computer Aided Diagnosis System-A Decision Support System for Clinical Diagnosis of Brain Tumours. International Journal of Computational Intelligence Systems. 10(1). 104–104. 13 indexed citations
14.
Sachdeva, Jainy, Vinod Kumar, Indra Gupta, Niranjan Khandelwal, & Chirag Ahuja. (2013). Segmentation, Feature Extraction, and Multiclass Brain Tumor Classification. Journal of Digital Imaging. 26(6). 1141–1150. 160 indexed citations
15.
Sachdeva, Jainy, Vinod Kumar, Indra Gupta, Niranjan Khandelwal, & Chirag Ahuja. (2012). Hybrid model for 2-D rigid multimodal registration of brain images. 20(2). 67–90. 1 indexed citations
16.
Sachdeva, Jainy, Vinod Kumar, Indra Gupta, Niranjan Khandelwal, & Chirag Ahuja. (2012). A novel content-based active contour model for brain tumor segmentation. Magnetic Resonance Imaging. 30(5). 694–715. 94 indexed citations
17.
Sachdeva, Jainy, Vinod Kumar, Indra Gupta, Niranjan Khandelwal, & Chirag Ahuja. (2012). A dual neural network ensemble approach for multiclass brain tumor classification. International Journal for Numerical Methods in Biomedical Engineering. 28(11). 1107–1120. 20 indexed citations
18.
Sachdeva, Jainy, Vinod Kumar, Indra Gupta, Niranjan Khandelwal, & Chirag Ahuja. (2011). Multiclass Brain Tumor Classification Using GA-SVM. 182–187. 37 indexed citations
19.
Kumar, Vinod, Jainy Sachdeva, Indra Gupta, Niranjan Khandelwal, & Chirag Ahuja. (2011). Classification of brain tumors using PCA-ANN. 1079–1083. 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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